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Bibliography on: Brain-Computer Interface

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ESP: PubMed Auto Bibliography 17 Sep 2021 at 01:31 Created: 

Brain-Computer Interface

Wikipedia: A brain–computer interface (BCI), sometimes called a neural control interface (NCI), mind–machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature. BCI-effected sensory input: Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s. BCI-effected motor output: When artificial intelligence is used to decode neural activity, then send that decoded information to some kind of effector device, BCIs have the potential to restore communication to people who have lost the ability to move or speak. To date, the focus has largely been on motor skills such as reaching or grasping. However, in May of 2021 a study showed that an AI/BCI system could be use to translate thoughts about handwriting into the output of legible characters at a usable rate (90 characters per minute with 94% accuracy).

Created with PubMed® Query: bci OR (brain-computer interface) OR (brain-machine interface) OR (mind-machine interface) OR (neural-control interface) NOT 26799652[PMID] NOT pmcbook NOT ispreviousversion

Citations The Papers (from PubMed®)

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RevDate: 2021-09-15
CmpDate: 2021-09-15

Abend A, Steele C, Jahnke HG, et al (2021)

Adhesion of Neurons and Glial Cells with Nanocolumnar TiN Films for Brain-Machine Interfaces.

International journal of molecular sciences, 22(16):.

Coupling of cells to biomaterials is a prerequisite for most biomedical applications; e.g., neuroelectrodes can only stimulate brain tissue in vivo if the electric signal is transferred to neurons attached to the electrodes' surface. Besides, cell survival in vitro also depends on the interaction of cells with the underlying substrate materials; in vitro assays such as multielectrode arrays determine cellular behavior by electrical coupling to the adherent cells. In our study, we investigated the interaction of neurons and glial cells with different electrode materials such as TiN and nanocolumnar TiN surfaces in contrast to gold and ITO substrates. Employing single-cell force spectroscopy, we quantified short-term interaction forces between neuron-like cells (SH-SY5Y cells) and glial cells (U-87 MG cells) for the different materials and contact times. Additionally, results were compared to the spreading dynamics of cells for different culture times as a function of the underlying substrate. The adhesion behavior of glial cells was almost independent of the biomaterial and the maximum growth areas were already seen after one day; however, adhesion dynamics of neurons relied on culture material and time. Neurons spread much better on TiN and nanocolumnar TiN and also formed more neurites after three days in culture. Our designed nanocolumnar TiN offers the possibility for building miniaturized microelectrode arrays for impedance spectroscopy without losing detection sensitivity due to a lowered self-impedance of the electrode. Hence, our results show that this biomaterial promotes adhesion and spreading of neurons and glial cells, which are important for many biomedical applications in vitro and in vivo.

RevDate: 2021-09-14

Alfiero CJ, Brooks SJ, Bideganeta HM, et al (2021)

Protein Supplementation Does Not Improve Aerobic and Anaerobic Fitness in Collegiate Dancers Performing Cycling Based High Intensity Interval Training.

Journal of dance medicine & science : official publication of the International Association for Dance Medicine & Science [Epub ahead of print].

The effects of a 6-week cycling high-intensity interval training (HIIT) concurrently with protein supplementation on aerobic and anaerobic fitness and body composition in collegiate dancers was investigated. Eighteen participants enrolled in a collegiate dance program were matched into three groups: high-protein (HP; 90 g·d-1), moderate-protein (MP; 40 g·d-1), and control (C; 0 g·d-1). All participants performed a 6-week HIIT intervention. Participants completed a graded exercise test, Wingate anaerobic test (Wingate), and dual energy x-ray absorptiometry scan before and after the intervention. Peak heart rate (HRpeak), peak oxygen uptake (VOpeak), peak power output (PPO), lactate threshold (LT), and ventilatory thresholds 1 (VT1) and 2 (VT2) were assessed during the graded exercise test. Peak power output, mean power output (MPO), and fatigue index (FI) were assessed during the Wingate. Lean mass (LM), fat mass (FM), visceral adipose tissue, appendicular skeletal muscle mass, and appendicular skeletal muscle mass index were assessed during dual energy x-ray absorptiometry. Body composition index (BCI) was calculated from pre and post LM and FM. Habitual diet was recorded weekly. Significance was set at p ≤ 0.05. No significant differences in VO2peak and percent fat mass (%FM) were observed between groups prior to the intervention. Significant main effects for time were observed for HRpeak (p = 0.02), VO2peak (p < 0.001), PPO (p < 0.01), LT (p < 0.001), VT1 (p < 0.001), and VT2 (p < 0.001) during the graded exercise test, and PPO (p < 0.01) and FI (p < 0.01) during the Wingate. Significant main effects for time were observed for LM (kg; p = 0.01) and FM (kg; p < 0.01). Body composition index was improved for all groups, however, no significant differences by group were observed. No significant differences were observed between groups for the measured outcomes (p > 0.05). Therefore, there was no effect of protein supplementation in the short 6-week intervention. This cycling based HIIT routine increased physical fitness, optimized aesthetics, and was a simple addition to an existing collegiate dance curriculum.

RevDate: 2021-09-13

Zhang S, Yan X, Wang Y, et al (2021)

Modulation of brain states on fractal and oscillatory power of EEG in brain-computer interfaces.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Electroencephalogram (EEG) is an objective reflection of the brain activities, which provides potential possibilities for brain state estimation based on EEG characteristics. However, how to mine the effective EEG characteristics is still a distressing problem in brain state monitoring.

APPROACH: The phase-scrambled method was used to generate images with different noise levels. Images were encoded into a rapid serial visual presentation (RSVP) paradigm. N-back working memory method was employed to induce and assess fatigue state. The irregular-resampling auto-spectral analysis (IRASA) method was adopted to extract and parameterize (exponent and offset) the characteristics of EEG fractal components, which were analyzed in the four dimensions: fatigue, sustained attention, visual noise and experimental tasks.

RESULTS: The degree of fatigue and visual noise level had positive effects on exponent and offset in the prefrontal lobe, and the ability of sustained attention negatively affected exponent and offset. Compared with visual stimuli task, rest task induced even larger values of exponent and offset and statistically significant in the most cerebral cortex. In addition, the SSVEP amplitudes were negatively and positively affected by the degree of fatigue and noise levels, respectively.

SIGNIFICANCE: The conclusions of this study provide insights into the relationship between brain states and EEG characteristics. In addition, this study has the potential to provide objective methods for brain states monitoring and EEG modeling.

RevDate: 2021-09-13

Ping-Ju L, Tianyu J, Li C, et al (2021)

CNN-based prognosis of BCI rehabilitation using EEG from first session BCI training.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Stroke is a world-leading disease for causing disability. Brain-computer interaction (BCI) training has been proved to be a promising method in facilitating motor recovery. However, due to differences in each patient's neural-clinical profile, the potential of recovery for different patients can vary significantly by conducting BCI training, which remains a major problem in clinical rehabilitation practice. To address this issue, the objective of this study is to prognosticate the outcome of BCI training using motor state electroencephalographic (EEG) collected during the first session of BCI tasks, with the aim of prescribing BCI training accordingly. A Convolution Neural Network (CNN) based prognosis model was developed to predict the outcome of 11 stroke patients' recovery following a 2-week rehabilitation training with BCI. In our study, functional connectivity and power spectrum have been evaluated and applied as the inputs of CNN to regress patients' recovery rate. A saliency map was used to identify the correlation between EEG channels with the recovery outcome. The performance of our model was assessed using the leave-one-out cross-validation. Overall, the proposed model predicted patients' recovery with R2 0.98 and MSE 0.89. According to the saliency map, the highest functional connectivity occurred in Fp2/Fpz-AF8, Fp2/F4/F8-P3, P1/PO7-PO5 and AF3-AF4. Our results demonstrated that deep learning method has the potential to predict the recovery rate of BCI training, which contributes to guiding individualized prescription in the early stage of clinical rehabilitation.

RevDate: 2021-09-13

Glikmann-Johnston Y, Mercieca EC, Carmichael AM, et al (2021)

Hippocampal and striatal volumes correlate with spatial memory impairment in Huntington's disease.

Journal of neuroscience research [Epub ahead of print].

Spatial memory impairments are observed in people with Huntington's disease (HD), however, the domain of spatial memory has received little focus when characterizing the cognitive phenotype of HD. Spatial memory is traditionally thought to be a hippocampal-dependent function, while the neuropathology of HD centers on the striatum. Alongside spatial memory deficits in HD, recent neurocognitive theories suggest that a larger brain network is involved, including the striatum. We examined the relationship between hippocampal and striatal volumes and spatial memory in 36 HD gene expansion carriers, including premanifest (n = 24) and early manifest HD (n = 12), and 32 matched healthy controls. We assessed spatial memory with Paired Associates Learning, Rey-Osterrieth Complex Figure Test, and the Virtual House task, which assesses three components of spatial memory: navigation, object location, and plan drawing. Caudate nucleus, putamen, and hippocampal volumes were manually segmented on T1-weighted MR images. As expected, caudate nucleus and putamen volumes were significantly smaller in the HD group compared to controls, with manifest HD having more severe atrophy than the premanifest HD group. Hippocampal volumes did not differ significantly between HD and control groups. Nonetheless, on average, the HD group performed significantly worse than controls across all spatial memory tasks. The spatial memory components of object location and recall of figural and topographical drawings were associated with striatal and hippocampal volumes in the HD cohort. We provide a case to include spatial memory impairments in the cognitive phenotype of HD, and extend the neurocognitive picture of HD beyond its primary pathology within the striatum.

RevDate: 2021-09-13

Guan S, Li J, Wang F, et al (2021)

Discriminating three motor imagery states of the same joint for brain-computer interface.

PeerJ, 9:e12027 pii:12027.

The classification of electroencephalography (EEG) induced by the same joint is one of the major challenges for brain-computer interface (BCI) systems. In this paper, we propose a new framework, which includes two parts, feature extraction and classification. Based on local mean decomposition (LMD), cloud model, and common spatial pattern (CSP), a feature extraction method called LMD-CSP is proposed to extract distinguishable features. In order to improve the classification results multi-objective grey wolf optimization twin support vector machine (MOGWO-TWSVM) is applied to discriminate the extracted features. We evaluated the performance of the proposed framework on our laboratory data sets with three motor imagery (MI) tasks of the same joint (shoulder abduction, extension, and flexion), and the average classification accuracy was 91.27%. Further comparison with several widely used methods showed that the proposed method had better performance in feature extraction and pattern classification. Overall, this study can be used for developing high-performance BCI systems, enabling individuals to control external devices intuitively and naturally.

RevDate: 2021-09-13

Sun B, W Zhao (2021)

Compressed Sensing of Extracellular Neurophysiology Signals: A Review.

Frontiers in neuroscience, 15:682063.

This article presents a comprehensive survey of literature on the compressed sensing (CS) of neurophysiology signals. CS is a promising technique to achieve high-fidelity, low-rate, and hardware-efficient neural signal compression tasks for wireless streaming of massively parallel neural recording channels in next-generation neural interface technologies. The main objective is to provide a timely retrospective on applying the CS theory to the extracellular brain signals in the past decade. We will present a comprehensive review on the CS-based neural recording system architecture, the CS encoder hardware exploration and implementation, the sparse representation of neural signals, and the signal reconstruction algorithms. Deep learning-based CS methods are also discussed and compared with the traditional CS-based approaches. We will also extend our discussion to cover the technical challenges and prospects in this emerging field.

RevDate: 2021-09-12

Rubio-Tomás T (2021)

Novel insights into SMYD2 and SMYD3 inhibitors: from potential anti-tumoural therapy to a variety of new applications.

Molecular biology reports [Epub ahead of print].

The revelance of the epigenetic regulation of cancer led to the design and testing of many drugs targeting epigenetic modifiers. The Su(Var)3-9, Enhancer-of-zeste and Trithorax (SET) and myeloid, Nervy, and DEAF-1 (MYND) domain-containing protein 2 (SMYD2) and 3 (SMYD3) are methyltransferases which act on histone and non-histone proteins to promote tumorigenesis in many cancer types. In addition to their oncogenic roles, SMYD2 and SMYD3 are involved in many other physiopathological conditions. In this review we will focus on the advances made in the last five years in the field of pharmacology regarding drugs targeting SMYD2 (such as LLY-507 or AZ505) and SMYD3 (such as BCI-121 or EPZ031686) and their potential cellular and molecular mechanisms of action and application in anti-tumoural therapy and/or against other diseases.

RevDate: 2021-09-11

Hou Y, Chen T, Lun X, et al (2021)

A novel method for classification of multi-class motor imagery tasks based on feature fusion.

Neuroscience research pii:S0168-0102(21)00204-2 [Epub ahead of print].

Motor Imagery based Brain computer interface (MI-BCI) has the advantage of strong independence that can rely on the spontaneous brain activity of the user to operate external devices. However, MI-BCI still has the problem of poor control effect, which requires more effective feature extraction algorithms and classification methods to extract distinctly separable features from electroencephalogram (EEG) signals. This paper proposes a novel framework based on Bispectrum, Entropy and common spatial pattern (BECSP). Here we use three methods of bispectrum in higher order spectra, entropy and CSP to extract MI-EEG signal features, and then select the most contributing features through tree-based feature selection algorithm. By comparing the classification results of SVM, Random Forest, Naive Bayes, LDA, KNN, Xgboost and Adaboost, we finally decide to use the SVM algorithm based on RBF kernel function which obtained the best result among them for classification. The proposed method is applied to the BCI competition IV data set 2a and BCI competition III data set IVa. On data set 2a, the highest accuracy on the evaluation data set reaches 85%. The experiment on data set IVa can also achieve good results. Compared with other algorithms that use the same data set, the performance of our algorithm has also been improved.

RevDate: 2021-09-10

Si X, Li S, Xiang S, et al (2021)

Imagined speech increases the hemodynamic response and functional connectivity of the dorsal motor cortex.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Decoding imagined speech from brain signals could provide a more natural, user-friendly way for developing the next generation of the brain-computer interface (BCI). With the advantages of non-invasive, portable, relatively high spatial resolution and insensitivity to motion artifacts, the functional near-infrared spectroscopy (fNIRS) shows great potential for developing the non-invasive speech BCI. However, there is a lack of fNIRS evidence in uncovering the neural mechanism of imagined speech. Our goal is to investigate the specific brain regions and the corresponding cortico-cortical functional connectivity features during imagined speech with fNIRS.

APPROACH: fNIRS signals were recorded from thirteen subjects' bilateral motor and prefrontal cortex during overtly and covertly repeating words. Cortical activation was determined through the mean oxygen-hemoglobin concentration changes, and functional connectivity was calculated by Pearson's correlation coefficient.

MAIN RESULTS: (1) The bilateral dorsal motor cortex was significantly activated during the covert speech, whereas the bilateral ventral motor cortex was significantly activated during the overt speech. (2) As a subregion of the motor cortex, sensorimotor cortex (SMC) showed a dominant dorsal response to covert speech condition, whereas a dominant ventral response to overt speech condition. (3) Broca's area was deactivated during the covert speech but activated during the overt speech. (4) Compared to overt speech, dorsal SMC-related functional connections were enhanced during the covert speech.

SIGNIFICANCE: We provide fNIRS evidence for the involvement of dSMC in speech imagery. dSMC is the speech imagery network's key hub and is probably involved in the sensorimotor information processing during the covert speech. This study could inspire the BCI community to focus on the potential contribution of dSMC during speech imagery.

RevDate: 2021-09-10

Zhang Y, Le S, Li H, et al (2021)

MRI magnetic compatible electrical neural interface: From materials to application.

Biosensors & bioelectronics, 194:113592 pii:S0956-5663(21)00629-1 [Epub ahead of print].

Neural electrical interfaces are important tools for local neural stimulation and recording, which potentially have wide application in the diagnosis and treatment of neural diseases, as well as in the transmission of neural activity for brain-computer interface (BCI) systems. At the same time, magnetic resonance imaging (MRI) is one of the effective and non-invasive techniques for recording whole-brain signals, providing details of brain structures and also activation pattern maps. Simultaneous recording of extracellular neural signals and MRI combines two expressions of the same neural activity and is believed to be of great importance for the understanding of brain function. However, this combination makes requests on the magnetic and electronic performance of neural interface devices. MRI-compatibility refers here to a technological approach to simultaneous MRI and electrode recording or stimulation without artifacts in imaging. Trade-offs between materials magnetic susceptibility selection and electrical function should be considered. Herein, prominent trends in selecting materials of suitable magnetic properties are analyzed and material design, function and application of neural interfaces are outlined together with the remaining challenge to fabricate MRI-compatible neural interface.

RevDate: 2021-09-10

Zhang XN, Meng QH, Zeng M, et al (2021)

Decoding Olfactory EEG Signals for Different Odor Stimuli Identification Using Wavelet-Spatial Domain Feature.

Journal of neuroscience methods pii:S0165-0270(21)00290-9 [Epub ahead of print].

BACKGROUND: Decoding olfactory-induced electroencephalography (olfactory EEG) signals has gained significant attention in recent years, owing to its potential applications in several fields, such as disease diagnosis, multimedia applications, and brain-computer interaction (BCI). Extracting discriminative features from olfactory EEG signals with low spatial resolution and poor signal-to-noise ratio is vital but challenging for improving decoding accuracy.

NEW METHODS: By combining discrete wavelet transform (DWT) with one-versus-rest common spatial pattern (OVR-CSP), we develop a novel feature, named wavelet-spatial domain feature (WSDF), to decode the olfactory EEG signals. First, DWT is employed on EEG signals for multilevel wavelet decomposition. Next, the DWT coefficients obtained at a specific level are subjected to OVR-CSP for spatial filtering. Correspondingly, the variance is extracted to generate a discriminative feature set, labeled as WSDF.

RESULTS: To verify the effectiveness of WSDF, a classification of olfactory EEG signals was conducted on two data sets, i.e., a public EEG dataset 'Odor Pleasantness Perception Dataset (OPPD)', and a self-collected dataset, by using support vector machine (SVM) trained based on different cross-validation methods. Experimental results showed that on OPPD dataset, the proposed method achieved a best average accuracy of 100% and 94.47% for the eyes-open and eyes-closed conditions, respectively. Moreover, on our own dataset, the proposed method gave a highest average accuracy of 99.50%.

Compared with a wide range of EEG features and existing works on the same dataset, our WSDF yielded superior classification performance.

CONCLUSIONS: The proposed WSDF is a promising candidate for decoding olfactory EEG signals.

RevDate: 2021-09-10

Bisht A, Singh P, Kaur C, et al (2021)

Progress and Challenges in Physiological Artifacts' Detection in Electroencephalographic Readings.

Current medical imaging pii:CMIR-EPUB-117769 [Epub ahead of print].

BACKGROUND: Electroencephalographic (EEG) recordings are used to trace neural activity within the cortex to study brain functioning over time.

INTRODUCTION: During data acquisition, the unequivocal way to reduce artifact is to avoid artifact stimulating events. Though there are certain artifacts that make this task challenging due to their association with the internal human mechanism, in the human-computer interface, these physiological artifacts are of great assistance and act as a command signal for controlling a device or an application (communication). That is why pre-processing of electroencephalographic readings has been a progressive area of exploration, as none of the published work can be viewed as a benchmark for constructive artifact handling.

METHOD: This review offers a comprehensive insight into state of the art physiological artifact removal techniques listed so far. The study commences from the single-stage traditional techniques to the multistage techniques, examining the pros and cons of each discussed technique. Also, this review paper gives a general idea of various datasets available and briefs the topical trend in EEG signal processing.

RESULT: Comparing the state of the art techniques with hybrid ones on the basis of performance and computational complexity, it has been observed that the single-channel techniques save computational time but lack in effective artifact removal especially physiological artifacts. On the other hand, hybrid techniques merge the essential characteristics resulting in increased performance, but time consumption and complexity remain an issue.

CONCLUSION: Considering the high probability of the presence of multiple artifacts in EEG channels, a trade-off between performance, time and computational complexity is the only key for effective processing of artifacts in the time ahead. This paper is anticipated to facilitate upcoming researchers in enriching the contemporary artifact handling techniques to mitigate the expert's burden.

RevDate: 2021-09-10

Kim S, Lee S, Kang H, et al (2021)

P300 Brain-Computer Interface-Based Drone Control in Virtual and Augmented Reality.

Sensors (Basel, Switzerland), 21(17): pii:s21175765.

Since the emergence of head-mounted displays (HMDs), researchers have attempted to introduce virtual and augmented reality (VR, AR) in brain-computer interface (BCI) studies. However, there is a lack of studies that incorporate both AR and VR to compare the performance in the two environments. Therefore, it is necessary to develop a BCI application that can be used in both VR and AR to allow BCI performance to be compared in the two environments. In this study, we developed an opensource-based drone control application using P300-based BCI, which can be used in both VR and AR. Twenty healthy subjects participated in the experiment with this application. They were asked to control the drone in two environments and filled out questionnaires before and after the experiment. We found no significant (p > 0.05) difference in online performance (classification accuracy and amplitude/latency of P300 component) and user experience (satisfaction about time length, program, environment, interest, difficulty, immersion, and feeling of self-control) between VR and AR. This indicates that the P300 BCI paradigm is relatively reliable and may work well in various situations.

RevDate: 2021-09-10

Mridha MF, Das SC, Kabir MM, et al (2021)

Brain-Computer Interface: Advancement and Challenges.

Sensors (Basel, Switzerland), 21(17): pii:s21175746.

Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.

RevDate: 2021-09-10

Appriou A, Pillette L, Trocellier D, et al (2021)

BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification.

Sensors (Basel, Switzerland), 21(17): pii:s21175740.

Research on brain-computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithms before using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals.

RevDate: 2021-09-10

Monini S, Filippi C, De Luca A, et al (2021)

On the Effect of Bimodal Rehabilitation in Asymmetric Hearing Loss.

Journal of clinical medicine, 10(17): pii:jcm10173927.

BACKGROUND: Bone conductive implants (BCI) have been reported to provide greater beneficial effects for the auditory and perceptual functions of the contralateral ear in patients presenting with asymmetric hearing loss (AHL) compared to those with single-sided deafness (SSD). The aim of the study was to assess the effects of wearing a conventional hearing aid in the contralateral ear on BCI in terms of an improved overall auditory performance.

METHODS: eleven AHL subjects wearing a BCI in their worse hearing ear underwent an auditory evaluation by pure tone and speech audiometry in free field. This study group was obtained by adding to the AHL patients those SSD subjects that, during the follow-up, showed deterioration of the hearing threshold of the contralateral ear, thus presenting with the features of AHL. Four different conditions were tested and compared: unaided, with BCI only, with contralateral hearing aid (CHA) only and with BCI combined with CHA.

RESULTS: all of the prosthetic conditions caused a significant improvement with respect to the unaided condition. When a CHA was adopted, its combination with the BCI showed significantly better auditory performances than those achieved with the BCI only.

CONCLUSIONS: the present study suggests the beneficial role of a CHA in BCI-implanted AHL subjects in terms of overall auditory performance.

RevDate: 2021-09-09

Nason SR, Mender MJ, Vaskov AK, et al (2021)

Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface.

Neuron pii:S0896-6273(21)00604-8 [Epub ahead of print].

Modern brain-machine interfaces can return function to people with paralysis, but current upper extremity brain-machine interfaces are unable to reproduce control of individuated finger movements. Here, for the first time, we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utilizes intracortical neural signals to bridge this gap. We created a non-prehensile task that systematically individuates two finger groups, the index finger and the middle-ring-small fingers combined. During online brain control, the ReFIT Kalman filter could predict individuated finger group movements with high performance. Next, training ridge regression decoders with individual movements was sufficient to predict untrained combined movements and vice versa. Finally, we compared the postural and movement tuning of finger-related cortical activity to find that individual cortical units simultaneously encode multiple behavioral dimensions. Our results suggest that linear decoders may be sufficient for brain-machine interfaces to execute high-dimensional tasks with the performance levels required for naturalistic neural prostheses.

RevDate: 2021-09-09

Singh AK, Sahonero-Alvarez G, Mahmud M, et al (2021)

Towards Bridging the Gap Between Computational Intelligence and Neuroscience in Brain-Computer Interfaces With a Common Description of Systems and Data.

Frontiers in neuroinformatics, 15:699840.

RevDate: 2021-09-08

Guney OB, Oblokulov M, H Ozkan (2021)

A Deep Neural Network for SSVEP-based Brain-Computer Interfaces.

IEEE transactions on bio-medical engineering, PP: [Epub ahead of print].

OBJECTIVE: Target identification in brain-computer interface (BCI) spellers refers to the electroencephalogram (EEG) classification for predicting the target character that the subject intends to spell. When the visual stimulus of each character is tagged with a distinct frequency, the EEG records steady-state visually evoked potentials (SSVEP) whose spectrum is dominated by the harmonics of the target frequency. In this setting, we address the target identification and propose a novel deep neural network (DNN) architecture.

METHOD: The proposed DNN processes the multi-channel SSVEP with convolutions across the sub-bands of harmonics, channels, time, and classifies at the fully connected layer. We test with two publicly available large scale (the benchmark and BETA) datasets consisting of in total 105 subjects with 40 characters. Our first stage training learns a global model by exploiting the statistical commonalities among all subjects, and the second stage fine tunes to each subject separately by exploiting the individualities.

RESULTS: Our DNN achieves impressive information transfer rates (ITRs) on both datasets, 265.23 bits/min and 196.59 bits/min, respectively, with only 0.4 seconds of stimulation. The code is available for reproducibility at https://github.com/osmanberke/Deep-SSVEP-BCI.

CONCLUSION: The presented DNN strongly outperforms the state-of-the-art techniques as our accuracy and ITR rates are the highest ever reported performance results on these datasets.

SIGNIFICANCE: Due to its unprecedentedly high speller ITRs and flawless applicability to general SSVEP systems, our technique has great potential in various biomedical engineering settings of BCIs such as communication, rehabilitation and control.

RevDate: 2021-09-07

Chen R, Xu G, Zheng Y, et al (2021)

Waveform feature extraction and signal recovery in single-channel TVEP based on Fitzhugh-Nagumo stochastic resonance.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Transient visual evoked potential (TVEP) can reflect the condition of the visual pathway and has been widely used in brain-computer interface. TVEP signals are typically obtained by averaging the time-locked brain responses across dozens or even hundreds of stimulations, in order to remove different kinds of interferences. However, this procedure increases the time needed to detect the brain status in realistic applications. Meanwhile, long repeated stimuli can vary the evoked potentials and discomfort the subjects. Therefore, a novel unsupervised framework was developed in this study to realize the fast extraction of single-channel TVEP signals with a high signal-to-noise ratio.

APPROACH: Using the principle of nonlinear aperiodic FHN model, a fast extraction and signal restoration technology of TVEP waveform based on FHN stochastic resonance is proposed to achieve high-quality acquisition of signal features with less average times.

RESULTS: A synergistic effect produced by noise, aperiodic signal and nonlinear system can force the energy of noise to be transferred into TVEP and hence amplifying the useful P100 feature while suppressing multi-scale noise.

SIGNIFICANCE: Compared with the conventional average and average-SSA-ICA method, the average-FHN method has a shorter stimulation time which can greatly improve the comfort of patients in clinical TVEP detection and a better performance of TVEP waveform i.e. a higher accuracy of P100 latency. The FHN recovery method is not only highly correlated with the original signal, but also can better highlight the P100 amplitude, which has high clinical application value.

RevDate: 2021-09-07

Zhang Y, Cai H, Nie L, et al (2021)

An end-to-end 3D convolutional neural network for decoding attentive mental state.

Neural networks : the official journal of the International Neural Network Society, 144:129-137 pii:S0893-6080(21)00324-5 [Epub ahead of print].

The detection of attentive mental state plays an essential role in the neurofeedback process and the treatment of Attention Deficit and Hyperactivity Disorder (ADHD). However, the performance of the detection methods is still not satisfactory. One of the challenges is to find a proper representation for the electroencephalogram (EEG) data, which could preserve the temporal information and maintain the spatial topological characteristics. Inspired by the deep learning (DL) methods in the research of brain-computer interface (BCI) field, a 3D representation of EEG signal was introduced into attention detection task, and a 3D convolutional neural network model with cascade and parallel convolution operations was proposed. The model utilized three cascade blocks, each consisting of two parallel 3D convolution branches, to simultaneously extract the multi-scale features. Evaluated on a public dataset containing twenty-six subjects, the proposed model achieved better performance compared with the baseline methods under the intra-subject, inter-subject and subject-adaptive classification scenarios. This study demonstrated the promising potential of the 3D CNN model for detecting attentive mental state.

RevDate: 2021-09-07

Ali A, Tariq H, Abbas S, et al (2021)

Draft genome sequence of a multidrug-resistant novel candidate Pseudomonas sp. NCCP-436T isolated from feces of a bovine host in Pakistan.

Journal of global antimicrobial resistance pii:S2213-7165(21)00203-4 [Epub ahead of print].

OBJECTIVES: The genus Pseudomonas comprised of Gram-negative strains with a variety of prospective industrial applications plus a growing pathogen that reported to produce nosocomial infections worldwide. The current study describes the first draft genome sequence analysis of a CRISPR-carrying, multidrug resistant, candidate novel Pseudomonas sp. NCCP-436T that was isolated from fecal sample of a neonatal calf and endowed with zoonotic potential.

METHODS: 16S rRNA gene was used to identify Pseudomonas strain NCCP-436T. The disk diffusion technique was applied to assess antimicrobial susceptibility as per the Clinical and Laboratory Standards Institute (CLSI) recommendations (M100). The genome of strain NCCP-436T was sequenced using an Illumina NovaSeq PE150 platform and analyzed using various bioinformatic tools. The virulence factors and resistome were identified using the Pathosystems Resource Integration Center (PATRIC) and Comprehensive Antibiotic Resistance Database (CARD) servers, while the CGView Server was used to set up a circular map of the strain NCCP-436T genome.

RESULTS: The draft genome of Pseudomonas strain NCCP-436T contained 43 contigs with a total genome size of 3,683,517 bp (61.4% GC content). There were 3,452 predicted genes, including 60 tRNA, 7 rRNA (five copies of 5S and single copies each of 16S and 23S) and 12 sRNA, as well as three genomic islands. CRISPR analyses revealed two CRISPR arrays with lengths of 1103 and 867 bp on the positive strand of scaffold 1, while functional annotation demonstrated the absence of prophage-related genes. Repetitive DNA analysis confirmed the existence of 57 tandemly repetitive DNA sequences, while minisatellite DNA and microsatellite DNA were located in numbers of 41 and 5, respectively. A total of 72 genes that encode the carbohydrate active enzymes were identified, whereas determination of secondary metabolites revealed 65 secondary metabolites genes distributed on 2 gene clusters i.e., non-ribosomal peptide synthetase cluster (NRPS) and β-lactone cluster. Antibiotic susceptibility showed that strain NCCP-436T was highly resistant to fluoroquinolones, beta lactams, cephalosporins, aminoglycosides, penicillins, rifamycins, macrolides, glycopeptides and trimethoprim-sulfonamide antibiotic classes. Additionally, 22 antibiotic resistance genes (ARGs), 313 virulence genes and 253 pathogen-host interactor genes were predicted in Pseudomonas sp. NCCP-436T genome. The comparison of average nucleotide identity (ANI) and digital DNA-DNA hybridization (digi-DDH) values with the closely related strain Pseudomonas khazarica (TBZ2) was found to be 82.08 and 34.90 %, respectively. These findings depict that strain NCCP-436T could potentially be a new species of Pseudomonas genus.

CONCLUSION: In conclusion, the draft genome of a candidate novel strain of Pseudomonas sp. NCCP-436T was successfully sequenced and assembled. Substantial numbers of antibiotic resistance and virulence genes and homology to human pathogens were predicted, exposing its pathogenic and zoonotic ability to public health. All of these data could be used to conduct more research into multidrug resistance mechanisms, leading to a better understanding of the genomic epidemiological features and drug resistance mechanisms of pathogenic Pseudomonas species in Pakistan.

RevDate: 2021-09-07

Shan B, Pu Y, Chen B, et al (2021)

New Technologies' Commercialization: The Roles of the Leader's Emotion and Incubation Support.

Frontiers in psychology, 12:710122.

New technologies, such as brain-computer interfaces technology, advanced artificial intelligence, cloud computing, and virtual reality technology, have a strong influence on our daily activities. The application and commercialization of these technologies are prevailing globally, such as distance education, health monitoring, smart home devices, and robots. However, we still know little about the roles of individual emotion and the external environment on the commercialization of these new technologies. Therefore, we focus on the emotional factor of the leader, which is their passion for work, and discuss its effect on technology commercialization. We also analyzed the moderating role of incubation support in the relationship between the leader's emotion and technology commercialization. The results contribute to the application of emotion in improving the commercialization of new technologies.

RevDate: 2021-09-07

Huang X, Xu Y, Hua J, et al (2021)

A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain-Computer Interface.

Frontiers in neuroscience, 15:733546.

In an electroencephalogram- (EEG-) based brain-computer interface (BCI), a subject can directly communicate with an electronic device using his EEG signals in a safe and convenient way. However, the sensitivity to noise/artifact and the non-stationarity of EEG signals result in high inter-subject/session variability. Therefore, each subject usually spends long and tedious calibration time in building a subject-specific classifier. To solve this problem, we review existing signal processing approaches, including transfer learning (TL), semi-supervised learning (SSL), and a combination of TL and SSL. Cross-subject TL can transfer amounts of labeled samples from different source subjects for the target subject. Moreover, Cross-session/task/device TL can reduce the calibration time of the subject for the target session, task, or device by importing the labeled samples from the source sessions, tasks, or devices. SSL simultaneously utilizes the labeled and unlabeled samples from the target subject. The combination of TL and SSL can take advantage of each other. For each kind of signal processing approaches, we introduce their concepts and representative methods. The experimental results show that TL, SSL, and their combination can obtain good classification performance by effectively utilizing the samples available. In the end, we draw a conclusion and point to research directions in the future.

RevDate: 2021-09-07

Shupe LE, Miles FP, Jones G, et al (2021)

Neurochip3: An Autonomous Multichannel Bidirectional Brain-Computer Interface for Closed-Loop Activity-Dependent Stimulation.

Frontiers in neuroscience, 15:718465.

Toward addressing many neuroprosthetic applications, the Neurochip3 (NC3) is a multichannel bidirectional brain-computer interface that operates autonomously and can support closed-loop activity-dependent stimulation. It consists of four circuit boards populated with off-the-shelf components and is sufficiently compact to be carried on the head of a non-human primate (NHP). NC3 has six main components: (1) an analog front-end with an Intan biophysical signal amplifier (16 differential or 32 single-ended channels) and a 3-axis accelerometer, (2) a digital control system comprised of a Cyclone V FPGA and Atmel SAM4 MCU, (3) a micro SD Card for 128 GB or more storage, (4) a 6-channel differential stimulator with ±60 V compliance, (5) a rechargeable battery pack supporting autonomous operation for up to 24 h and, (6) infrared transceiver and serial ports for communication. The NC3 and earlier versions have been successfully deployed in many closed-loop operations to induce synaptic plasticity and bridge lost biological connections, as well as deliver activity-dependent intracranial reinforcement. These paradigms to strengthen or replace impaired connections have many applications in neuroprosthetics and neurorehabilitation.

RevDate: 2021-09-06

Douibi K, Le Bars S, Lemontey A, et al (2021)

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications.

Frontiers in human neuroscience, 15:705064.

In the last few decades, Brain-Computer Interface (BCI) research has focused predominantly on clinical applications, notably to enable severely disabled people to interact with the environment. However, recent studies rely mostly on the use of non-invasive electroencephalographic (EEG) devices, suggesting that BCI might be ready to be used outside laboratories. In particular, Industry 4.0 is a rapidly evolving sector that aims to restructure traditional methods by deploying digital tools and cyber-physical systems. BCI-based solutions are attracting increasing attention in this field to support industrial performance by optimizing the cognitive load of industrial operators, facilitating human-robot interactions, and make operations in critical conditions more secure. Although these advancements seem promising, numerous aspects must be considered before developing any operational solutions. Indeed, the development of novel applications outside optimal laboratory conditions raises many challenges. In the current study, we carried out a detailed literature review to investigate the main challenges and present criteria relevant to the future deployment of BCI applications for Industry 4.0.

RevDate: 2021-09-06

Zhou Q, Lin J, Yao L, et al (2021)

Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects.

Frontiers in human neuroscience, 15:701091.

One of the most significant challenges in the application of brain-computer interfaces (BCI) is the large performance variation, which often occurs over time or across users. Recent evidence suggests that the physiological states may explain this performance variation in BCI, however, the underlying neurophysiological mechanism is unclear. In this study, we conducted a seven-session motor-imagery (MI) experiment on 20 healthy subjects to investigate the neurophysiological mechanism on the performance variation. The classification accuracy was calculated offline by common spatial pattern (CSP) and support vector machine (SVM) algorithms to measure the MI performance of each subject and session. Relative Power (RP) values from different rhythms and task stages were used to reflect the physiological states and their correlation with the BCI performance was investigated. Results showed that the alpha band RP from the supplementary motor area (SMA) within a few seconds before MI was positively correlated with performance. Besides, the changes of RP between task and pre-task stage from theta, alpha, and gamma band were also found to be correlated with performance both across time and subjects. These findings reveal a neurophysiological manifestation of the performance variations, and would further provide a way to improve the BCI performance.

RevDate: 2021-09-06

Bouton C, Bhagat N, Chandrasekaran S, et al (2021)

Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand.

Frontiers in neuroscience, 15:699631.

Millions of people worldwide suffer motor or sensory impairment due to stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, diabetes, and motor neuron diseases such as ALS (amyotrophic lateral sclerosis). A brain-computer interface (BCI), which links the brain directly to a computer, offers a new way to study the brain and potentially restore impairments in patients living with these debilitating conditions. One of the challenges currently facing BCI technology, however, is to minimize surgical risk while maintaining efficacy. Minimally invasive techniques, such as stereoelectroencephalography (SEEG) have become more widely used in clinical applications in epilepsy patients since they can lead to fewer complications. SEEG depth electrodes also give access to sulcal and white matter areas of the brain but have not been widely studied in brain-computer interfaces. Here we show the first demonstration of decoding sulcal and subcortical activity related to both movement and tactile sensation in the human hand. Furthermore, we have compared decoding performance in SEEG-based depth recordings versus those obtained with electrocorticography electrodes (ECoG) placed on gyri. Initial poor decoding performance and the observation that most neural modulation patterns varied in amplitude trial-to-trial and were transient (significantly shorter than the sustained finger movements studied), led to the development of a feature selection method based on a repeatability metric using temporal correlation. An algorithm based on temporal correlation was developed to isolate features that consistently repeated (required for accurate decoding) and possessed information content related to movement or touch-related stimuli. We subsequently used these features, along with deep learning methods, to automatically classify various motor and sensory events for individual fingers with high accuracy. Repeating features were found in sulcal, gyral, and white matter areas and were predominantly phasic or phasic-tonic across a wide frequency range for both HD (high density) ECoG and SEEG recordings. These findings motivated the use of long short-term memory (LSTM) recurrent neural networks (RNNs) which are well-suited to handling transient input features. Combining temporal correlation-based feature selection with LSTM yielded decoding accuracies of up to 92.04 ± 1.51% for hand movements, up to 91.69 ± 0.49% for individual finger movements, and up to 83.49 ± 0.72% for focal tactile stimuli to individual finger pads while using a relatively small number of SEEG electrodes. These findings may lead to a new class of minimally invasive brain-computer interface systems in the future, increasing its applicability to a wide variety of conditions.

RevDate: 2021-09-06

Kang JH, Youn J, Kim SH, et al (2021)

Effects of Frontal Theta Rhythms in a Prior Resting State on the Subsequent Motor Imagery Brain-Computer Interface Performance.

Frontiers in neuroscience, 15:663101.

Dealing with subjects who are unable to attain a proper level of performance, that is, those with brain-computer interface (BCI) illiteracy or BCI inefficients, is still a major issue in human electroencephalography (EEG) BCI systems. The most suitable approach to address this issue is to analyze the EEG signals of individual subjects independently recorded before the main BCI tasks, to evaluate their performance on these tasks. This study mainly focused on non-linear analyses and deep learning techniques to investigate the significant relationship between the intrinsic characteristics of a prior idle resting state and the subsequent BCI performance. To achieve this main objective, a public EEG motor/movement imagery dataset that constituted two individual EEG signals recorded from an idle resting state and a motor imagery BCI task was used in this study. For the EEG processing in the prior resting state, spectral analysis but also non-linear analyses, such as sample entropy, permutation entropy, and recurrent quantification analyses (RQA), were performed to obtain individual groups of EEG features to represent intrinsic EEG characteristics in the subject. For the EEG signals in the BCI tasks, four individual decoding methods, as a filter-bank common spatial pattern-based classifier and three types of convolution neural network-based classifiers, quantified the subsequent BCI performance in the subject. Statistical linear regression and ANOVA with post hoc analyses verified the significant relationship between non-linear EEG features in the prior resting state and three types of BCI performance as low-, intermediate-, and high-performance groups that were statistically discriminated by the subsequent BCI performance. As a result, we found that the frontal theta rhythm ranging from 4 to 8 Hz during the eyes open condition was highly associated with the subsequent BCI performance. The RQA findings that higher determinism and lower mean recurrent time were mainly observed in higher-performance groups indicate that more regular and stable properties in the EEG signals over the frontal regions during the prior resting state would provide a critical clue to assess an individual BCI ability in the following motor imagery task.

RevDate: 2021-09-03

Senathirajah Y, Hribar M, Section Editors of the IMIA Yearbook Section on Human Factors and Organizational Issues (2021)

Human Factors and Organizational Issues Section Synopsis IMIA Yearbook 2021.

Yearbook of medical informatics, 30(1):100-104.

OBJECTIVE: To select the best papers that made original and high impact contributions in the area of human factors and organizational issues in biomedical informatics in 2020.

METHODS: A rigorous extraction process based on queries from Web of Science® and PubMed/Medline was conducted to identify the scientific contributions published in 2020 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two section editors led to a total of 1,562 papers. These papers were discussed for a selection of 12 finalist papers, which were then reviewed by the two section editors, two chief editors, and by three external reviewers from internationally renowned research teams.

RESULTS: The query process resulted in 12 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces. This year three papers were clearly outstanding and help advance in the field. They provide examples of applying existing frameworks together in novel and highly illuminating ways, showing the value of theory development in human factors. Emerging themes included several which discussed physician burnout, mobile health, and health equity. Those concerning the Corona Virus Disease 2019 (Covid-19) were included as part of that section.

CONCLUSION: The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.

RevDate: 2021-09-03

Erdoğan SB, Yükselen G, Yegül MM, et al (2021)

Identification of impulsive adolescents with a functional near infrared spectroscopy (fNIRS) based decision support system.

Journal of neural engineering [Epub ahead of print].

BACKGROUND: The gold standard for diagnosing impulsivity relies on clinical interviews, behavioral questionnaires and rating scales which are highly subjective.

OBJECTIVE: The aim of this study was to develop a functional near infrared spectroscopy (fNIRS) based classification approach for correct identification of impulsive adolescents. Taking into account the multifaceted nature of impulsivity, we propose that combining informative features from clinical, behavioral and neurophysiological domains might better elucidate the neurobiological distinction underlying symptoms of impulsivity.

APPROACH: Hemodynamic and behavioral information was collected from 38 impulsive adolescents and from 33 non-impulsive adolescents during a Stroop task with concurrent fNIRS recordings. Connectivity-based features were computed from the hemodynamic signals and a neural efficiency metric was computed by fusing the behavioral and connectivity-based features. We tested the efficacy of two commonly used supervised machine-learning methods, namely the support vector machines (SVM) and artificial neural networks (ANN) in discriminating impulsive adolescents from their non-impulsive peers when trained with multi-domain features. Wrapper method was adapted to identify the informative biomarkers in each domain. Classification accuracies of each algorithm were computed after 10 runs of a 10-fold cross-validation procedure, conducted for 7 different combinations of the 3-domain feature set.

MAIN RESULTS: Both SVM and ANN achieved diagnostic accuracies above 90% when trained with Wrapper-selected clinical, behavioral and fNIRS derived features. SVM performed significantly higher than ANN in terms of the accuracy metric (%92.2 and %90.16 respectively, p=0.005).

SIGNIFICANCE: Preliminary findings show the feasibility and applicability of both machine-learning based methods for correct identification of impulsive adolescents when trained with multi-domain data involving clinical interviews, fNIRS based biomarkers and neuropsychiatric test measures. The proposed automated classification approach holds promise for assisting the clinical practice of diagnosing impulsivity and other psychiatric disorders. Our results also pave the path for a computer-aided diagnosis perspective for rating the severity of impulsivity.

RevDate: 2021-09-03

Haddix C, Al-Bakri AF, S Sunderam (2021)

Prediction of isometric handgrip force from graded event-related desynchronization of the sensorimotor rhythm.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Brain-computer interfaces (BCIs) show promise as a direct line of communication between the brain and the outside world that could benefit those with impaired motor function. The commands available for BCI operation are often limited by the ability of the decoder to differentiate between the many distinct motor or cognitive tasks that can be visualized or attempted. Simple binary command signals (e.g. right hand at rest versus movement) are therefore used due to their ability to produce large observable differences in neural recordings. At the same time, frequent command switching can impose greater demands on the subject's focus and takes time to learn. Here, we attempt to decode the degree of effort in a specific movement task to produce a graded and more flexible command signal.

APPROACH: Fourteen healthy human subjects (9 male, 5 female) responded to visual cues by squeezing a hand dynamometer to different levels of predetermined force, guided by continuous visual feedback, while the electroencephalogram (EEG) and grip force were monitored. Movement-related EEG features were extracted and modeled to predict exerted force.

MAIN RESULTS: We found that event-related desynchronization (ERD) of the 8-30 Hz mu-beta sensorimotor rhythm (SMR) of the EEG is separable for different degrees of motor effort. Upon four-fold cross-validation, linear classifiers were found to predict grip force from an ERD vector with mean accuracies across subjects of 53% and 55% for the dominant and non-dominant hand, respectively. ERD amplitude increased with target force but appeared to hit a trough that hinted at non-monotonic behavior.

SIGNIFICANCE: Our results suggest that modeling and interactive feedback based on the intended level of motor effort is feasible. The observed ERD trends suggest that different mechanisms may govern intermediate versus low and high degrees of motor effort. This may have utility in rehabilitative protocols for motor impairments.

RevDate: 2021-09-03

Al-Qazzaz NK, Alyasseri ZAA, Abdulkareem KH, et al (2021)

EEG feature fusion for motor imagery: A new robust framework towards stroke patients rehabilitation.

Computers in biology and medicine, 137:104799 pii:S0010-4825(21)00593-X [Epub ahead of print].

Stroke is the second foremost cause of death worldwide and is one of the most common causes of disability. Several approaches have been proposed to manage stroke patient rehabilitation such as robotic devices and virtual reality systems, and researchers have found that the brain-computer interfaces (BCI) approaches can provide better results. Therefore, the most challenging tasks with BCI applications involve identifying the best technique(s) that can reveal the neuron stimulus information from the patients' brains and extracting the most effective features from these signals as well. Accordingly, the main novelty of this paper is twofold: propose a new feature fusion method for motor imagery (MI)-based BCI and develop an automatic MI framework to detect the changes pre- and post-rehabilitation. This study investigated the electroencephalography (EEG) dataset from post-stroke patients with upper extremity hemiparesis. All patients performed 25 MI-based BCI sessions with follow up assessment visits to examine the functional changes before and after EEG neurorehabilitation. In the first stage, conventional filters and automatic independent component analysis with wavelet transform (AICA-WT) denoising technique were used. Next, attributes from time, entropy and frequency domains were computed, and the effective features were combined into time-entropy-frequency (TEF) attributes. Consequently, the AICA-WT and the TEF fusion set were utilised to develop an AICA-WT-TEF framework. Then, support vector machine (SVM), k-nearest neighbours (kNN) and random forest (RF) classification technique were tested for MI-based BCI rehabilitation. The proposed AICA-WT-TEF framework with RF classifier achieves the best results compared with other classifiers. Finally, the proposed framework and feature fusion set achieve a significant performance in terms of accuracy measures compared to the state-of-the-art. Therefore, the proposed methods could be crucial for improving the process of automatic MI rehabilitation and are recommended for implementation in real-time applications.

RevDate: 2021-09-03

Zhang B, Yuan P, Xu G, et al (2021)

DUSP6 expression is associated with osteoporosis through the regulation of osteoclast differentiation via ERK2/Smad2 signaling.

Cell death & disease, 12(9):825.

Osteoporosis-related fractures, such as femoral neck and vertebral fractures, are common in aged people, resulting in increased disability rate and health-care costs. Thus, it is of great importance to clarify the mechanism of osteoclast-related osteoporosis and find effective ways to avoid its complication. In this study, gene expression profile analysis and real-time polymerase chain reaction revealed that DUSP6 expression was suppressed in human and mice osteoporosis cases. In vitro experiments confirmed that DUSP6 overexpression prevented osteoclastogenesis, whereas inhibition of DUSP6 by small interference RNA or with a chemical inhibitor, (E/Z)-BCI, had the opposite effect. (E/Z)-BCl significantly accelerated the bone loss process in vivo by enhancing osteoclastogenesis. Bioinformatics analyses and in vitro experiments indicated that miR-181a was an upstream regulator of DUSP6. Moreover, miR-181a positively induced the differentiation and negatively regulated the apoptosis of osteoclasts via DUSP6. Furthermore, downstream signals by ERK2 and SMAD2 were also found to be involved in this process. Evaluation of ERK2-deficiency bone marrow-derived macrophages confirmed the role of ERK2 signaling in the DUSP6-mediated osteoclastogenesis. Additionally, immunoprecipitation assays confirmed that DUSP6 directly modified the phosphorylation status of SMAD2 and the subsequent nuclear transportation of NFATC1 to regulate osteoclast differentiation. Altogether, this study demonstrated for the first time the role of miRNA-181a/DUSP6 in the progression of osteoporosis via the ERK2 and SMAD2 signaling pathway. Hence, DUSP6 may represent a novel target for the treatment of osteoclast-related diseases in the future.

RevDate: 2021-09-02

Shen Y, Wang J, S Navlakha (2021)

A Correspondence between Normalization Strategies in Artificial and Biological Neural Networks.

Neural computation pii:107074 [Epub ahead of print].

A fundamental challenge at the interface of machine learning and neuroscience is to uncover computational principles that are shared between artificial and biological neural networks. In deep learning, normalization methods such as batch normalization, weight normalization, and their many variants help to stabilize hidden unit activity and accelerate network training, and these methods have been called one of the most important recent innovations for optimizing deep networks. In the brain, homeostatic plasticity represents a set of mechanisms that also stabilize and normalize network activity to lie within certain ranges, and these mechanisms are critical for maintaining normal brain function. In this article, we discuss parallels between artificial and biological normalization methods at four spatial scales: normalization of a single neuron's activity, normalization of synaptic weights of a neuron, normalization of a layer of neurons, and normalization of a network of neurons. We argue that both types of methods are functionally equivalent-that is, both push activation patterns of hidden units toward a homeostatic state, where all neurons are equally used-and we argue that such representations can improve coding capacity, discrimination, and regularization. As a proof of concept, we develop an algorithm, inspired by a neural normalization technique called synaptic scaling, and show that this algorithm performs competitively against existing normalization methods on several data sets. Overall, we hope this bidirectional connection will inspire neuroscientists and machine learners in three ways: to uncover new normalization algorithms based on established neurobiological principles; to help quantify the trade-offs of different homeostatic plasticity mechanisms used in the brain; and to offer insights about how stability may not hinder, but may actually promote, plasticity.

RevDate: 2021-09-02

Xu R, Spataro R, Allison BZ, et al (2021)

Brain-Computer Interfaces in Acute and Subacute Disorders of Consciousness.

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society pii:00004691-900000000-99262 [Epub ahead of print].

SUMMARY: Disorders of consciousness include coma, unresponsive wakefulness syndrome (also known as vegetative state), and minimally conscious state. Neurobehavioral scales such as coma recovery scale-revised are the gold standard for disorder of consciousness assessment. Brain-computer interfaces have been emerging as an alternative tool for these patients. The application of brain-computer interfaces in disorders of consciousness can be divided into four fields: assessment, communication, prediction, and rehabilitation. The operational theoretical model of consciousness that brain-computer interfaces explore was reviewed in this article, with a focus on studies with acute and subacute patients. We then proposed a clinically friendly guideline, which could contribute to the implementation of brain-computer interfaces in neurorehabilitation settings. Finally, we discussed limitations and future directions, including major challenges and possible solutions.

RevDate: 2021-09-02

Zhang H, Zhu L, Xu S, et al (2021)

Two Brains, One Target: Design of a Multi-level Information Fusion Model Based on Dual-subject RSVP.

Journal of neuroscience methods pii:S0165-0270(21)00281-8 [Epub ahead of print].

BACKGROUND: Rapid serial visual presentation (RSVP) based brain-computer interface (BCI) is widely used to categorize the target and non-target images. The available information limits the prediction accuracy of single-trial using single-subject electroencephalography (EEG) signals. New Method. Hyperscanning is a new manner to record two or more subjects' signals simultaneously. So we designed a multi-level information fusion model for target image detection based on dual-subject RSVP, namely HyperscanNet. The two modules of this model fuse the data and features of the two subjects at the data and feature layers. A chunked long and short-term memory artificial neural network (LSTM) was used in the time dimension to extract features at different periods separately, completing fine-grained underlying feature extraction. While the feature layer is fused, some plain operations are used to complete the fusion of the data layer to ensure that important information is not missed.

RESULTS: Experimental results show that the F1-score (the harmonic mean of precision and recall) of this method with best group of channels and segment length is 82.76%. Comparison with existing methods. This method improves the F1-score by at least 5% compared to single-subject target detection.

CONCLUSIONS: Target detection can be accomplished by the two subjects' collaboration to achieve a higher and more stable F1-score than a single subject.

RevDate: 2021-09-02

Waqar M, Mohamed S, Dulhanty L, et al (2021)

Radiologically defined acute hydrocephalus in aneurysmal subarachnoid haemorrhage.

British journal of neurosurgery [Epub ahead of print].

BACKGROUND: Ventriculomegaly is common in aneurysmal subarachnoid haemorrhage (aSAH). An imaging measure to predict the need for cerebrospinal fluid (CSF) diversion may be useful. The bicaudate index (BCI) has been previously applied to aSAH. Our aim was to derive and test a threshold BCI above which CSF diversion may be required.

METHODS: Review of prospective registry. The derivation group (2009-2015) included WFNS grade 1-2 aSAH patients who deteriorated clinically, had a repeat CT brain and underwent CSF diversion. BCI was measured on post-deterioration CTs and the lower limit of the 95% confidence interval (95%CI) was the hydrocephalus threshold. In a separate test group (2016), in WFNS ≥ 2 patients, we compared BCI on diagnostic CTs with CSF diversion within 24 hours.

RESULTS: The derivation group (n = 62) received an external ventricular (n = 57, 92%) or lumbar drain (n = 5, 8%). Mean post-deterioration BCI was 0.19 (95%CI 0.17-0.22) for age ≤49 years, 0.22 (95%CI 0.20-0.23) for age 50-64 years and 0.24 (95%CI 0.22-0.27) for age ≥65 years. Hydrocephalus thresholds were therefore 0.17, 0.20 and 0.22, respectively. In the test group (n = 105), there was no significant difference in BCI on the diagnostic CT between good and poor grade patients aged ≤49 years (p = 0.31) and ≥65 years (p = 0.96). 30/66 WFNS ≥ 2 patients underwent CSF diversion, although only 15/30 (50%) exceeded BCI thresholds for hydrocephalus.

CONCLUSION: A significant proportion of aSAH patients may undergo CSF diversion without objective evidence of hydrocephalus. Our threshold values require further testing but may provide an objective measure to aid clinical decision making in aSAH.

RevDate: 2021-09-02

Tarasenko A, Oganesyan M, Ivaskevych D, et al (2020)

Artificial Intelligence, Brains, and Beyond: Imperial College London Neurotechnology Symposium, 2020.

Bioelectricity, 2(3):310-313.

In this report, we give an overview of the proceedings from the online Imperial College London Neurotechnology Symposium 2020. The first part deals with the fundamentals of how artificial intelligence (AI) can be used to inform research frameworks used in the field of neurotechnology. The second part goes a level higher and shows how AI can be used in cutting-edge cellular and molecular methodologies and their applications. The final part focuses on the efforts to "decode" neural systems in brain-computer interfaces to advance neuroprosthetics.

RevDate: 2021-08-31
CmpDate: 2021-08-31

Yan W, Du C, Wu Y, et al (2021)

SSVEP-EEG Denoising via Image Filtering Methods.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 29:1634-1643.

Steady-state visual evoked potential (SSVEP) is widely used in electroencephalogram (EEG) control, medical detection, cognitive neuroscience, and other fields. However, successful application requires improving the detection performance of SSVEP signal frequency characteristics. Most strategies to enhance the signal-to-noise ratio of SSVEP utilize application of a spatial filter. Here, we propose a method for image filtering denoising (IFD) of the SSVEP signal from the perspective of image analysis, as a preprocessing step for signal analysis. Arithmetic mean, geometric mean, Gaussian, and non-local means filtering methods were tested, and the experimental results show that image filtering of SSVEP cannot effectively remove the noise. Thus, we proposed a reverse solution in which the SSVEP noise signal was obtained by image filtering, and then the noise was subtracted from the original signal. Comparison of the recognition accuracy of the SSVEP signal before and after denoising was used to evaluate the denoising performance for stimuli of different duration. After IFD processing, SSVEP exhibited higher recognition accuracy, indicating the effectiveness of this proposed denoising method. Application of this method improves the detection performance of SSVEP signal frequency characteristics, combines image processing and brain signal analysis, and expands the research scope of brain signal analysis for widespread application.

RevDate: 2021-08-30

Jovanovic LI, Popovic MR, C Marquez-Chin (2021)

Characterizing the stimulation interference in electroencephalographic signals during brain-computer interface-controlled functional electrical stimulation therapy.

Artificial organs [Epub ahead of print].

INTRODUCTION: The integration of brain-computer interface (BCI) and functional electrical stimulation (FES) has brought about a new rehabilitation strategy: BCI-controlled FES therapy or BCI-FEST. During BCI-FEST, the stimulation is triggered by the patient's brain activity, often monitored using electroencephalography (EEG). Several studies have demonstrated that BCI-FEST can improve voluntary arm and hand function after an injury, but few studies have investigated the FES interference in EEG signals during BCI-FEST. In this study, we evaluated the effectiveness of band-pass filters, used to extract the BCI-relevant EEG components, in simultaneously reducing stimulation interference.

METHODS: We used EEG data from eight participants recorded during BCI-FEST. Additionally, we separately recorded the FES signal generated by the stimulator to estimate the spectral components of the FES interference, and extract the noise in time domain. Finally, we calculated signal-to-noise ratio (SNR) values before and after band-pass filtering, for two types of movements practiced during BCI-FEST: reaching and grasping.

RESULTS: The SNR values were greater after filtering across all participants for both movement types. For reaching movements, mean SNR values increased between 1.31dB and 36.3dB. Similarly, for grasping movements, mean SNR values increased between 2.82dB and 40.16dB, after filtering.

CONCLUSIONS: Band-pass filters, used to isolate EEG frequency bands for BCI application, were also effective in reducing stimulation interference. In addition, we provide a general algorithm that can be used in future studies to estimate the frequencies of FES interference as a function of FSTIM and FS .

RevDate: 2021-08-30

Sharini H, Zolghadriha S, Riyahi Alam N, et al (2021)

Assessment of Motor Cortex in Active, Passive and Imagery Wrist Movement Using Functional MRI.

Journal of biomedical physics & engineering, 11(4):515-526 pii:JBPE-11-4.

Background: Functional Magnetic resonance imaging (fMRI) measures the small fluctuation of blood flow happening during task-fMRI in brain regions.

Objective: This research investigated these active, imagery and passive movements in volunteers design to permit a comparison of their capabilities in activating the brain areas.

Material and Methods: In this applied research, the activity of the motor cortex during the right-wrist movement was evaluated in 10 normal volunteers under active, passive, and imagery conditions. T2* weighted, three-dimensional functional images were acquired using a BOLD sensitive gradient-echo EPI (echo planar imaging) sequence with echo time (TE) of 30 ms and repetition time (TR) of 2000 ms. The functional data, which included 248 volumes per subject and condition, were acquired using the blocked design paradigm. The images were analyzed by the SPM12 toolbox, MATLAB software.

Results: The findings determined a significant increase in signal intensity of the motor cortex while performing the test compared to the rest time (p< 0.05). It was also observed that the active areas in hand representation of the motor cortex are different in terms of locations and the number of voxels in different wrist directions. Moreover, the findings showed that the position of active centers in the brain is different in active, passive, and imagery conditions.

Conclusion: Results confirm that primary motor cortex neurons play an essential role in the processing of complex information and are designed to control the direction of movement. It seems that the findings of this study can be applied for rehabilitation studies.

RevDate: 2021-08-30

Chakraborty S, Saetta G, Simon C, et al (2021)

Could Brain-Computer Interface Be a New Therapeutic Approach for Body Integrity Dysphoria?.

Frontiers in human neuroscience, 15:699830.

Patients suffering from body integrity dysphoria (BID) desire to become disabled, arising from a mismatch between the desired body and the physical body. We focus here on the most common variant, characterized by the desire for amputation of a healthy limb. In most reported cases, amputation of the rejected limb entirely alleviates the distress of the condition and engenders substantial improvement in quality of life. Since BID can lead to life-long suffering, it is essential to identify an effective form of treatment that causes the least amount of alteration to the person's anatomical structure and functionality. Treatment methods involving medications, psychotherapy, and vestibular stimulation have proven largely ineffective. In this hypothesis article, we briefly discuss the characteristics, etiology, and current treatment options available for BID before highlighting the need for new, theory driven approaches. Drawing on recent findings relating to functional and structural brain correlates of BID, we introduce the idea of brain-computer interface (BCI)/neurofeedback approaches to target altered patterns of brain activity, promote re-ownership of the limb, and/or attenuate stress and negativity associated with the altered body representation.

RevDate: 2021-08-29

Kline A, Forkert ND, Felfeliyan B, et al (2021)

fMRI-Informed EEG for brain mapping of imagined lower limb movement: Feasibility of a brain computer interface.

Journal of neuroscience methods pii:S0165-0270(21)00274-0 [Epub ahead of print].

BACKGROUND: EEG and fMRI have contributed greatly to our understanding of brain activity and its link to behaviors by helping to identify both when and where the activity occurs. This is particularly important in the development of brain-computer interfaces (BCIs), where feed forward systems gather data from imagined brain activity and then send that information to an effector. The purpose of this study was to develop and evaluate a computational approach that enables an accurate mapping of spatial brain activity (fMRI) in relation to the temporal receptors (EEG electrodes) associated with imagined lower limb movement.

NEW METHOD: EEG and fMRI data from 16 healthy, male participants while imagining lower limb movement were used for this purpose. A combined analysis of fMRI data and EEG electrode locations was developed to identify EEG electrodes with a high likelihood of capturing imagined lower limb movement originating from various clusters of brain activity. This novel feature selection tool was used to develop an artificial neural network model to classify right and left lower limb movement.

RESULTS: Results showed that left versus right lower limb imagined movement could be classified with 66.5% accuracy using this approach. Comparison with existing methods: Adopting a purely data-driven approach for feature selection to use in the right/left classification task resulted in the same accuracy (66.6%) but with reduced interpretability.

CONCLUSIONS: The developed fMRI-informed EEG approach could pave the way towards improved brain computer interfaces for lower limb movement while also being applicable to other systems where fMRI could be helpful to inform EEG acquisition and processing.

RevDate: 2021-08-28

Verbaarschot C, Tump D, Lutu A, et al (2021)

A visual brain-computer interface as communication aid for patients with amyotrophic lateral sclerosis.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 132(10):2404-2415 pii:S1388-2457(21)00663-5 [Epub ahead of print].

OBJECTIVE: Brain-Computer Interface (BCI) spellers that make use of code-modulated Visual Evoked Potentials (cVEP) may provide a fast and more accurate alternative to existing visual BCI spellers for patients with Amyotrophic Lateral Sclerosis (ALS). However, so far the cVEP speller has only been tested on healthy participants.

METHODS: We assess the brain responses, BCI performance and user experience of the cVEP speller in 20 healthy participants and 10 ALS patients. All participants performed a cued and free spelling task, and a free selection of Yes/No answers.

RESULTS: 27 out of 30 participants could perform the cued spelling task with an average accuracy of 79% for ALS patients, 88% for healthy older participants and 94% for healthy young participants. All 30 participants could answer Yes/No questions freely, with an average accuracy of around 90%.

CONCLUSIONS: With ALS patients typing on average 10 characters per minute, the cVEP speller presented in this paper outperforms other visual BCI spellers.

SIGNIFICANCE: These results support a general usability of cVEP signals for ALS patients, which may extend far beyond the tested speller to control e.g. an alarm, automatic door, or TV within a smart home.

RevDate: 2021-08-28

Daly I (2021)

Removal of physiological artifacts from simultaneous EEG and fMRI recordings.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 132(10):2371-2383 pii:S1388-2457(21)00651-9 [Epub ahead of print].

OBJECTIVE: Simultaneous recording of the electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) allows a combination of eletrophysiological and haemodynamic information to be used to form a more complete picture of cerebral dynamics. However, EEG recorded within the MRI scanner is contaminated by both imaging artifacts and physiological artifacts. The majority of the techniques used to pre-process such EEG focus on removal of the imaging and balistocardiogram artifacts, with some success, but don't remove all other physiological artifacts.

METHODS: We propose a new offline EEG artifact removal method based upon a combination of independent component analysis and fMRI-based head movement estimation to aid the removal of physiological artifacts from EEG recorded during EEG-fMRI recordings. Our method makes novel use of head movement trajectories estimated from the fMRI recording in order to assist with identifying physiological artifacts in the EEG and is designed to be used after removal of the fMRI imaging artifact from the EEG.

RESULTS: We evaluate our method on EEG recorded during a joint EEG-fMRI session from healthy adult participants. Our method significantly reduces the influence of all types of physiological artifacts on the EEG. We also compare our method with a state-of-the-art physiological artifact removal method and demonstrate superior performance removing physiological artifacts.

CONCLUSIONS: Our proposed method is able to remove significantly more physiological artifact components from the EEG, recorded during a joint EEG-fMRI session, than other state-of-the-art methods.

SIGNIFICANCE: Our proposed method represents a marked improvement over current processing pipelines for removing physiological noise from EEG recorded during a joint EEG-fMRI session.

RevDate: 2021-08-28

Molina-Cantero AJ, Castro-García JA, Gómez-Bravo F, et al (2021)

Controlling a Mouse Pointer with a Single-Channel EEG Sensor.

Sensors (Basel, Switzerland), 21(16): pii:s21165481.

(1) Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse pointer and make selections through a combination of the user's attention level with the detection of voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A sequence of blinks allows for switching between these movement types, while the attention level modulates the cursor's speed. The influence of the attention level on performance was studied. Additionally, Fitts' model and the evolution of the emotional states of participants, among other trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups (Attention and No-Attention) performed three runs, on different days, in which 40 targets had to be reached and selected. Target positions and distances from the cursor's initial position were chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM) test and a final survey provided information about the system's usability and the emotions of participants during the experiment. (3) Results: The performance was similar to some brain-computer interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR) of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most of them assessed the experience positively. Movement times and hit rates were significantly better for participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible low-cost solution to manage a mouse pointer.

RevDate: 2021-08-28

Won K, Kwon M, Ahn M, et al (2021)

Selective Subject Pooling Strategy to Improve Model Generalization for a Motor Imagery BCI.

Sensors (Basel, Switzerland), 21(16): pii:s21165436.

Brain-computer interfaces (BCIs) facilitate communication for people who cannot move their own body. A BCI system requires a lengthy calibration phase to produce a reasonable classifier. To reduce the duration of the calibration phase, it is natural to attempt to create a subject-independent classifier with all subject datasets that are available; however, electroencephalogram (EEG) data have notable inter-subject variability. Thus, it is very challenging to achieve subject-independent BCI performance comparable to subject-specific BCI performance. In this study, we investigate the potential for achieving better subject-independent motor imagery BCI performance by conducting comparative performance tests with several selective subject pooling strategies (i.e., choosing subjects who yield reasonable performance selectively and using them for training) rather than using all subjects available. We observed that the selective subject pooling strategy worked reasonably well with public MI BCI datasets. Finally, based upon the findings, criteria to select subjects for subject-independent BCIs are proposed here.

RevDate: 2021-08-28

Ikeda A, Y Washizawa (2021)

Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks.

Sensors (Basel, Switzerland), 21(16): pii:s21165309.

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain-computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.

RevDate: 2021-08-28

De la Cruz-Guevara DR, Alfonso-Morales W, E Caicedo-Bravo (2021)

Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach.

Sensors (Basel, Switzerland), 21(16): pii:s21165308.

This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate-ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches-the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey's test.

RevDate: 2021-08-28

Holdengreber E, Yozevitch R, V Khavkin (2021)

Intuitive Cognition-Based Method for Generating Speech Using Hand Gestures.

Sensors (Basel, Switzerland), 21(16): pii:s21165291.

Muteness at its various levels is a common disability. Most of the technological solutions to the problem creates vocal speech through the transition from mute languages to vocal acoustic sounds. We present a new approach for creating speech: a technology that does not require prior knowledge of sign language. This technology is based on the most basic level of speech according to the phonetic division into vowels and consonants. The speech itself is expected to be expressed through sensing of the hand movements, as the movements are divided into three rotations: yaw, pitch, and roll. The proposed algorithm converts these rotations through programming to vowels and consonants. For the hand movement sensing, we used a depth camera and standard speakers in order to produce the sounds. The combination of the programmed depth camera and the speakers, together with the cognitive activity of the brain, is integrated into a unique speech interface. Using this interface, the user can develop speech through an intuitive cognitive process in accordance with the ongoing brain activity, similar to the natural use of the vocal cords. Based on the performance of the presented speech interface prototype, it is substantiated that the proposed device could be a solution for those suffering from speech disabilities.

RevDate: 2021-08-28

Zhang X, Hou W, Wu X, et al (2021)

Enhancing Detection of SSMVEP Induced by Action Observation Stimuli Based on Task-Related Component Analysis.

Sensors (Basel, Switzerland), 21(16): pii:s21165269.

Action observation (AO)-based brain-computer interface (BCI) is an important technology in stroke rehabilitation training. It has the advantage of simultaneously inducing steady-state motion visual evoked potential (SSMVEP) and activating sensorimotor rhythm. Moreover, SSMVEP could be utilized to perform classification. However, SSMVEP is composed of complex modulation frequencies. Traditional canonical correlation analysis (CCA) suffers from poor recognition performance in identifying those modulation frequencies at short stimulus duration. To address this issue, task-related component analysis (TRCA) was utilized to deal with SSMVEP for the first time. An interesting phenomenon was found: different modulated frequencies in SSMVEP distributed in different task-related components. On this basis, a multi-component TRCA method was proposed. All the significant task-related components were utilized to construct multiple spatial filters to enhance the detection of SSMVEP. Further, a combination of TRCA and CCA was proposed to utilize both advantages. Results showed that the accuracies using the proposed methods were significant higher than that using CCA at all window lengths and significantly higher than that using ensemble-TRCA at short window lengths (≤2 s). Therefore, the proposed methods further validate the induced modulation frequencies and will speed up the application of the AO-based BCI in rehabilitation.

RevDate: 2021-08-28

Dominguez-Paredes D, Jahanshahi A, KL Kozielski (2021)

Translational considerations for the design of untethered nanomaterials in human neural stimulation.

Brain stimulation, 14(5):1285-1297 pii:S1935-861X(21)00203-5 [Epub ahead of print].

Neural stimulation is a powerful tool to study brain physiology and an effective treatment for many neurological disorders. Conventional interfaces use electrodes implanted in the brain. As these are often invasive and have limited spatial targeting, they carry a potential risk of side-effects. Smaller neural devices may overcome these obstacles, and as such, the field of nanoscale and remotely powered neural stimulation devices is growing. This review will report on current untethered, injectable nanomaterial technologies intended for neural stimulation, with a focus on material-tissue interface engineering. We will review nanomaterials capable of wireless neural stimulation, and discuss their stimulation mechanisms. Taking cues from more established nanomaterial fields (e.g., cancer theranostics, drug delivery), we will then discuss methods to modify material interfaces with passive and bioactive coatings. We will discuss methods of delivery to a desired brain region, particularly in the context of how delivery and localization are affected by surface modification. We will also consider each of these aspects of nanoscale neurostimulators with a focus on their prospects for translation to clinical use.

RevDate: 2021-08-27

Yoshimura N, Umetsu K, Tonin A, et al (2021)

Binary Semantic Classification Using Cortical Activation with Pavlovian-Conditioned Vestibular Responses in Healthy and Locked-In Individuals.

Cerebral cortex communications, 2(3):tgab046 pii:tgab046.

To develop a more reliable brain-computer interface (BCI) for patients in the completely locked-in state (CLIS), here we propose a Pavlovian conditioning paradigm using galvanic vestibular stimulation (GVS), which can induce a strong sensation of equilibrium distortion in individuals. We hypothesized that associating two different sensations caused by two-directional GVS with the thoughts of "yes" and "no" by individuals would enable us to emphasize the differences in brain activity associated with the thoughts of yes and no and hence help us better distinguish the two from electroencephalography (EEG). We tested this hypothesis with 11 healthy and 1 CLIS participant. Our results showed that, first, conditioning of GVS with the thoughts of yes and no is possible. And second, the classification of whether an individual is thinking "yes" or "no" is significantly improved after the conditioning, even in the absence of subsequent GVS stimulations. We observed average classification accuracy of 73.0% over 11 healthy individuals and 85.3% with the CLIS patient. These results suggest the establishment of GVS-based Pavlovian conditioning and its usability as a noninvasive BCI.

RevDate: 2021-08-27

Nason SR, Mender MJ, Letner JG, et al (2021)

Restoring upper extremity function with brain-machine interfaces.

International review of neurobiology, 159:153-186.

One of the most exciting advances to emerge in neural interface technologies has been the development of real-time brain-machine interface (BMI) neuroprosthetic devices to restore upper extremity function. BMI neuroprostheses, made possible by synergistic advances in neural recording technologies, high-speed computation and signal processing, and neuroscience, have permitted the restoration of volitional movement to patients suffering the loss of upper-extremity function. In this chapter, we review the scientific and technological advances underlying these remarkable devices. After presenting an introduction to the current state of the field, we provide an accessible technical discussion of the two fundamental requirements of a successful neuroprosthesis: signal extraction from the brain and signal decoding that results in robust prosthetic control. We close with a presentation of emerging technologies that are likely to substantially advance the field.

RevDate: 2021-08-27

Sánchez-Cuesta FJ, Arroyo-Ferrer A, González-Zamorano Y, et al (2021)

Clinical Effects of Immersive Multimodal BCI-VR Training after Bilateral Neuromodulation with rTMS on Upper Limb Motor Recovery after Stroke. A Study Protocol for a Randomized Controlled Trial.

Medicina (Kaunas, Lithuania), 57(8): pii:medicina57080736.

Background and Objectives: The motor sequelae after a stroke are frequently persistent and cause a high degree of disability. Cortical ischemic or hemorrhagic strokes affecting the cortico-spinal pathways are known to cause a reduction of cortical excitability in the lesioned area not only for the local connectivity impairment but also due to a contralateral hemisphere inhibitory action. Non-invasive brain stimulation using high frequency repetitive magnetic transcranial stimulation (rTMS) over the lesioned hemisphere and contralateral cortical inhibition using low-frequency rTMS have been shown to increase the excitability of the lesioned hemisphere. Mental representation techniques, neurofeedback, and virtual reality have also been shown to increase cortical excitability and complement conventional rehabilitation. Materials and Methods: We aim to carry out a single-blind, randomized, controlled trial aiming to study the efficacy of immersive multimodal Brain-Computer Interfacing-Virtual Reality (BCI-VR) training after bilateral neuromodulation with rTMS on upper limb motor recovery after subacute stroke (>3 months) compared to neuromodulation combined with conventional motor imagery tasks. This study will include 42 subjects in a randomized controlled trial design. The main expected outcomes are changes in the Motricity Index of the Arm (MI), dynamometry of the upper limb, score according to Fugl-Meyer for upper limb (FMA-UE), and changes in the Stroke Impact Scale (SIS). The evaluation will be carried out before the intervention, after each intervention and 15 days after the last session. Conclusions: This trial will show the additive value of VR immersive motor imagery as an adjuvant therapy combined with a known effective neuromodulation approach opening new perspectives for clinical rehabilitation protocols.

RevDate: 2021-08-27
CmpDate: 2021-08-27

Pfotenhauer SM, Frahm N, Winickoff D, et al (2021)

Mobilizing the private sector for responsible innovation in neurotechnology.

Nature biotechnology, 39(6):661-664.

RevDate: 2021-08-27
CmpDate: 2021-08-27

Balaya V, Guimiot F, Bruzzi M, et al (2020)

Feasibility of a fetal anatomy 3D atlas by computer-assisted anatomic dissection.

Journal of gynecology obstetrics and human reproduction, 49(9):101880.

OBJECTIVE: To assess the feasibility of 3D modelisation of fetal anatomy by using the Computer-assisted anatomic dissection (CAAD) based on immunolabeled histologic slices and MRI slices with a specific 3D software.

STUDY DESIGN: For pelvis and lower limbs, subjects came from legal abortion, medical pregnancy termination, or late miscarriage. Specimens were fixed in 10 % formalin, then embedded in paraffin wax and serially sectioned. The histological slices were stained using HES and Masson Trichrome. Protein S-100 and D2-40 markers were used for immuno-labelling. Serial transverse sections were digitalized and manually aligned. Fetal brain slices were obtained from in utero or post-mortem MRI.

RESULTS: CAAD was performed on 10 fetuses: pelvis was modelised with 3 fetuses of 13, 15 and 24 W G, lower limbs with 2 fetuses of 14 and 15 W G and brain with 5 fetuses aged between 19 and 37 W G. Fetal pelvis innervation was analysed after immunolabelling and nerves appeared proportionally bigger than in adults with the same topography. Lower limbs analysis revealed that nerve development was guided by vascular development: the sciatic nerve along the big axial vein, the saphen nerve along the big saphen vein and the sural nerve along the small saphen vein. Fetal brain study allowed to describe the gyration process and the lateral ventricle development.

CONCLUSION: CAAD technique provides an accurate 3D reconstruction of fetal anatomy for lower limbs and pelvis but has to be improved for brain model since midline structures were not amendable for analysis. These results need to be confirmed with larger series of specimens at different stages of development.

RevDate: 2021-08-26

Almarri B, Rajasekaran S, CH Huang (2021)

Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI.

PloS one, 16(8):e0253383 pii:PONE-D-21-12874.

The dimensionality of the spatially distributed channels and the temporal resolution of electroencephalogram (EEG) based brain-computer interfaces (BCI) undermine emotion recognition models. Thus, prior to modeling such data, as the final stage of the learning pipeline, adequate preprocessing, transforming, and extracting temporal (i.e., time-series signals) and spatial (i.e., electrode channels) features are essential phases to recognize underlying human emotions. Conventionally, inter-subject variations are dealt with by avoiding the sources of variation (e.g., outliers) or turning the problem into a subject-deponent. We address this issue by preserving and learning from individual particularities in response to affective stimuli. This paper investigates and proposes a subject-independent emotion recognition framework that mitigates the subject-to-subject variability in such systems. Using an unsupervised feature selection algorithm, we reduce the feature space that is extracted from time-series signals. For the spatial features, we propose a subject-specific unsupervised learning algorithm that learns from inter-channel co-activation online. We tested this framework on real EEG benchmarks, namely DEAP, MAHNOB-HCI, and DREAMER. We train and test the selection outcomes using nested cross-validation and a support vector machine (SVM). We compared our results with the state-of-the-art subject-independent algorithms. Our results show an enhanced performance by accurately classifying human affection (i.e., based on valence and arousal) by 16%-27% compared to other studies. This work not only outperforms other subject-independent studies reported in the literature but also proposes an online analysis solution to affection recognition.

RevDate: 2021-08-26

Benatti HR, Luz HR, Lima DM, et al (2021)

Morphometric Patterns and Blood Biochemistry of Capybaras (Hydrochoerus hydrochaeris) from Human-Modified Landscapes and Natural Landscapes in Brazil.

Veterinary sciences, 8(8): pii:vetsci8080165.

The capybara, Hydrochoerus hydrochaeris, is the largest extant rodent of the world. To better understand the correlation between size and body mass, and biochemical parameters of capybaras from areas with different degrees of anthropization (i.e., different food supplies), we sampled free-ranging capybaras from areas of natural landscapes (NLs) and human-modified landscapes (HMLs) in Brazil. Analyses of biometrical and biochemical parameters of capybaras showed that animals from HMLs were heavier (higher body mass) than those from NL, a condition possibly related to fat deposit rather than body length, as indicated by Body Condition Index (BCI) analyses. Biochemical parameters indicated higher serum levels of albumin, creatine kinase, cholesterol, fructosamine and total protein among capybaras from HMLs than from NLs; however, when all adult capybaras were analyzed together only cholesterol and triglycerides were positively correlated with body mass. We propose that the biochemical profile differences between HMLs and NLs are related to the obesity condition of capybaras among HMLs. Considering that heavier animals might live longer and reproduce more often, our results could have important implications in the population dynamics of capybaras among HMLs, where this rodent species is frequently represented by overgrowth populations that generate several levels of conflicts with human beings.

RevDate: 2021-08-26

Mian SY, Honey JR, Carnicer-Lombarte A, et al (2021)

Large Animal Studies to Reduce the Foreign Body Reaction in Brain-Computer Interfaces: A Systematic Review.

Biosensors, 11(8): pii:bios11080275.

Brain-computer interfaces (BCI) are reliant on the interface between electrodes and neurons to function. The foreign body reaction (FBR) that occurs in response to electrodes in the brain alters this interface and may pollute detected signals, ultimately impeding BCI function. The size of the FBR is influenced by several key factors explored in this review; namely, (a) the size of the animal tested, (b) anatomical location of the BCI, (c) the electrode morphology and coating, (d) the mechanics of electrode insertion, and (e) pharmacological modification (e.g., drug eluting electrodes). Trialing methods to reduce FBR in vivo, particularly in large models, is important to enable further translation in humans, and we systematically reviewed the literature to this effect. The OVID, MEDLINE, EMBASE, SCOPUS and Scholar databases were searched. Compiled results were analysed qualitatively. Out of 8388 yielded articles, 13 were included for analysis, with most excluded studies experimenting on murine models. Cats, rabbits, and a variety of breeds of minipig/marmoset were trialed. On average, over 30% reduction in inflammatory cells of FBR on post mortem histology was noted across intervention groups. Similar strategies to those used in rodent models, including tip modification and flexible and sinusoidal electrode configurations, all produced good effects in histology; however, a notable absence of trials examining the effect on BCI end-function was noted. Future studies should assess whether the reduction in FBR correlates to an improvement in the functional effect of the intended BCI.

RevDate: 2021-08-26

Song X, Zeng Y, Tong L, et al (2021)

P3-MSDA: Multi-Source Domain Adaptation Network for Dynamic Visual Target Detection.

Frontiers in human neuroscience, 15:685173.

Single-trial electroencephalogram detection has been widely applied in brain-computer interface (BCI) systems. Moreover, an individual generalized model is significant for applying the dynamic visual target detection BCI system in real life because of the time jitter of the detection latency, the dynamics and complexity of visual background. Hence, we developed an unsupervised multi-source domain adaptation network (P3-MSDA) for dynamic visual target detection. In this network, a P3 map-clustering method was proposed for source domain selection. The adversarial domain adaptation was conducted for domain alignment to eliminate individual differences, and prediction probabilities were ranked and returned to guide the input of target samples for imbalanced data classification. The results showed that individuals with a strong P3 map selected by the proposed P3 map-clustering method perform best on the source domain. Compared with existing schemes, the proposed P3-MSDA network achieved the highest classification accuracy and F1 score using five labeled individuals with a strong P3 map as the source domain. These findings can have a significant meaning in building an individual generalized model for dynamic visual target detection.

RevDate: 2021-08-26

Fekri Azgomi H, Hahn JO, RT Faghih (2021)

Closed-Loop Fuzzy Energy Regulation in Patients With Hypercortisolism via Inhibitory and Excitatory Intermittent Actuation.

Frontiers in neuroscience, 15:695975.

Hypercortisolism or Cushing's disease, which corresponds to the excessive levels of cortisol hormone, is associated with tiredness and fatigue during the day and disturbed sleep at night. Our goal is to employ a wearable brain machine interface architecture to regulate one's energy levels in hypercortisolism. In the present simulation study, we generate multi-day cortisol profile data for ten subjects both in healthy and disease conditions. To relate an internal hidden cognitive energy state to one's cortisol secretion patterns, we employ a state-space model. Particularly, we consider circadian upper and lower bound envelopes on cortisol levels, and timings of hypothalamic pulsatile activity underlying cortisol secretions as continuous and binary observations, respectively. To estimate the hidden cognitive energy-related state, we use Bayesian filtering. In our proposed architecture, we infer one's cognitive energy-related state using wearable devices rather than monitoring the brain activity directly and close the loop utilizing fuzzy control. To model actuation in the real-time closed-loop architecture, we simulate two types of medications that result in increasing and decreasing the energy levels in the body. Finally, we close the loop using a knowledge-based control approach. The results on ten simulated profiles verify how the proposed architecture is able to track the energy state and regulate it using hypothetical medications. In a simulation study based on experimental data, we illustrate the feasibility of designing a wearable brain machine interface architecture for energy regulation in hypercortisolism. This simulation study is a first step toward the ultimate goal of managing hypercortisolism in real-world situations.

RevDate: 2021-08-26
CmpDate: 2021-08-26

Sugino C, Ruzzene M, A Erturk (2021)

Experimental and Computational Investigation of Guided Waves in a Human Skull.

Ultrasound in medicine & biology, 47(3):787-798.

We investigate guided (Lamb) waves in a human cadaver skull through experiments and computational simulations. Ultrasonic wedge transducers and scanning laser Doppler vibrometry are used respectively to excite and measure Lamb waves propagating in the cranial bone of a degassed skull. Measurements are performed over a section of the parietal bone and temporal bone spanning the squamous suture. The experimental data are analyzed for the identification of wave modes and the characterization of dispersion properties. In the parietal bone, for instance, the A0 wave mode is excited between 200 and 600 kHz, and higher-order Lamb waves are excited from 1 to 1.8 MHz. From the experimental dispersion curves and average thickness extracted from the skull computed tomography scan, we estimate average isotropic material properties that capture the essential dispersion characteristics using a semi-analytical finite-element model. We also explore the leaky and non-leaky wave behavior of the degassed skull with water loading in the cranial cavity. Successful excitation of leaky Lamb waves is confirmed (for higher-order wave modes with phase velocity faster than the speed of sound in water) from 500 kHz to 1.5 MHz, which may find applications in imaging and therapeutics at the brain periphery or skull-brain interface (e.g., for metastases). The non-leaky A0 Lamb wave mode propagates between 200 and 600 kHz, with or without fluid loading, for potential use in skull-related diagnostics and imaging (e.g., for sutures).

RevDate: 2021-08-25

Bruurmijn M, Raemaekers MAH, Branco MP, et al (2021)

Decoding attempted phantom hand movements from ipsilateral sensorimotor areas after amputation.

Journal of neural engineering [Epub ahead of print].

Objective The sensorimotor cortex is often selected as target in the development of a Brain-Computer Interface, as activation patterns from this region can be robustly decoded to discriminate between different movements the user executes. Up until recently, such BCIs were primarily based on activity in the contralateral hemisphere, where decoding movements still works even years after denervation. However, there is increasing evidence for a role of the sensorimotor cortex in controlling the ipsilateral body. The aim of this study is to investigate the effects of denervation on the movement representation on the ipsilateral sensorimotor cortex. Approach Eight subjects with acquired above-elbow arm amputation and nine controls performed a task in which they made (or attempted to make with their phantom hand) six different gestures from the American Manual Alphabet. Brain activity was measured using 7T functional MRI, and a classifier was trained to discriminate between activation patterns on four different regions of interest (ROIs) on the ipsilateral sensorimotor cortex. Main results Classification scores showed that decoding was possible and significantly better than chance level for both the phantom and intact hands from all ROIs. Decoding both the left (intact) and right (phantom) hand from the same hemisphere was also possible with above-chance level classification score. Significance The possibility to decode both hands from the same hemisphere, even years after denervation, indicates that implantation of motor-electrodes for BCI control possibly need only cover a single hemisphere, making surgery less invasive, and increasing options for people with lateralized damage to motor cortex like after stroke.

RevDate: 2021-08-25

Tabernig CB, Carrere LC, Biurrun Manresa J, et al (2021)

Does feedback based on FES-evoked nociceptive withdrawal reflex condition event-related desynchronization? An exploratory study with brain-computer interfaces.

Biomedical physics & engineering express [Epub ahead of print].

Introduction.Event-related desynchronization (ERD) is used in brain-computer interfaces (BCI) to detect the user's motor intention (MI) and convert it into a command for an actuator to provide sensory feedback or mobility, for example by means of functional electrical stimulation (FES). Recent studies have proposed to evoke the nociceptive withdrawal reflex (NWR) using FES, in order to evoke synergistic movements of the lower limb and to facilitate the gait rehabilitation of stroke patients. The use of NWR to provide sensorimotor feedback in ERD-based BCI is novel; thererfore, the conditioning effect that nociceptive stimuli might have on MI is still unknown.Objetive.To assess the ERD produced during the MI after FES-evoked NWR, in order to evaluate if nociceptive stimuli condition subsequent ERDs.Methods.Data from 528 electroencephalography trials of 8 healthy volunteers were recorded and analyzed. Volunteers used an ERD-based BCI, which provided two types of feedback: intrisic by the FES-evoked NWR and extrinsic by virtual reality. The electromyogram of the tibialis anterior muscle was also recorded. The main outcome variables were the normalized root mean square of the evoked electromyogram (RMSnorm), the average electroencephalogram amplitude at the ERD frequency during MI (A ̅MI) and the percentage decrease ofA ̅MIrelative to rest (ERD%) at the first MI subsequent to the activation of the BCI.Results.No evidence of changes of theRMSnormon both theA ̅MI(p=0.663) and theERD% (p=0.252) of the subsequent MI was detected. A main effect of the type of feedback was found in the subsequentA ̅MI(p <0.001), with intrinsic feedback resulting in a largerA ̅MI.Conclusions.No evidence of ERD conditioning was observed using BCI feedback based on FES-evoked NWR .Significance.FES-evoked NWR could constitute a potential feedback modality in an ERD-based BCI to facilitate motor recovery of stroke people.

RevDate: 2021-08-24

Neumann WJ, Sorkhabi MM, Benjaber M, et al (2021)

The sensitivity of ECG contamination to surgical implantation site in brain computer interfaces.

Brain stimulation pii:S1935-861X(21)00218-7 [Epub ahead of print].

BACKGROUND: Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability.

OBJECTIVES: Investigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity.

METHODS: Electric field modeling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination.

RESULTS: The impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination.

CONCLUSIONS: Given the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration.

RevDate: 2021-08-24

Bodenham RF, Mazeri S, Cleaveland S, et al (2021)

Latent class evaluation of the performance of serological tests for exposure to Brucella spp. in cattle, sheep, and goats in Tanzania.

PLoS neglected tropical diseases, 15(8):e0009630 pii:PNTD-D-21-00028.

BACKGROUND: Brucellosis is a neglected zoonosis endemic in many countries, including regions of sub-Saharan Africa. Evaluated diagnostic tools for the detection of exposure to Brucella spp. are important for disease surveillance and guiding prevention and control activities.

METHODS AND FINDINGS: Bayesian latent class analysis was used to evaluate performance of the Rose Bengal plate test (RBT) and a competitive ELISA (cELISA) in detecting Brucella spp. exposure at the individual animal-level for cattle, sheep, and goats in Tanzania. Median posterior estimates of RBT sensitivity were: 0.779 (95% Bayesian credibility interval (BCI): 0.570-0.894), 0.893 (0.636-0.989), and 0.807 (0.575-0.966), and for cELISA were: 0.623 (0.443-0.790), 0.409 (0.241-0.644), and 0.561 (0.376-0.713), for cattle, sheep, and goats, respectively. Sensitivity BCIs were wide, with the widest for cELISA in sheep. RBT and cELISA median posterior estimates of specificity were high across species models: RBT ranged between 0.989 (0.980-0.998) and 0.995 (0.985-0.999), and cELISA between 0.984 (0.974-0.995) and 0.996 (0.988-1). Each species model generated seroprevalence estimates for two livestock subpopulations, pastoralist and non-pastoralist. Pastoralist seroprevalence estimates were: 0.063 (0.045-0.090), 0.033 (0.018-0.049), and 0.051 (0.034-0.076), for cattle, sheep, and goats, respectively. Non-pastoralist seroprevalence estimates were below 0.01 for all species models. Series and parallel diagnostic approaches were evaluated. Parallel outperformed a series approach. Median posterior estimates for parallel testing were ≥0.920 (0.760-0.986) for sensitivity and ≥0.973 (0.955-0.992) for specificity, for all species models.

CONCLUSIONS: Our findings indicate that Brucella spp. surveillance in Tanzania using RBT and cELISA in parallel at the animal-level would give high test performance. There is a need to evaluate strategies for implementing parallel testing at the herd- and flock-level. Our findings can assist in generating robust Brucella spp. exposure estimates for livestock in Tanzania and wider sub-Saharan Africa. The adoption of locally evaluated robust diagnostic tests in setting-specific surveillance is an important step towards brucellosis prevention and control.

RevDate: 2021-08-24

Xu B, Wang Y, Deng L, et al (2021)

Decoding hand movement types and kinematic information from electroencephalogram.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Brain-computer interfaces (BCIs) have achieved successful control of assistive devices, e.g. neuroprosthesis or robotic arm. Previous research based on hand movements Electroencephalogram (EEG) has shown limited success in precise and natural control. In this study, we explored the possibilities of decoding movement types and kinematic information for three reach-and-execute actions using movement-related cortical potentials (MRCPs). EEG signals were acquired from 12 healthy subjects during the execution of pinch, palmar and precision disk rotation actions that involved two levels of speeds and forces. In the case of discrimination between hand movement types under each of four different kinematics conditions, we obtained the average peak accuracies of 83.44% and 73.83% for the binary and 3-class classification, respectively. In the case of discrimination between different movement kinematics for each of three actions, the average peak accuracies of 82.9% and 58.2% could be achieved for the two and 4-class scenario. In both cases, peak decoding performance was significantly higher than the subject-specific chance level. We found that hand movement types all could be classified when these actions were executed at four different kinematic parameters. Meanwhile, for each of three hand movements, we decoded movement parameters successfully. Furthermore, the feasibility of decoding hand movements during hand retraction process was also demonstrated. These findings are of great importance for controlling neuroprosthesis or other rehabilitation devices in a fine and natural way, which would drastically increase the acceptance of motor impaired users.

RevDate: 2021-08-24

Habibzadeh H, Norton JJS, Vaughan TM, et al (2021)

A Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-based BCIs.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

We present a dynamic window-length classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that does not require the user to choose a feature extraction method or channel set. Instead, the classifier uses multiple feature extraction methods and channel selections to infer the SSVEP and relies on majority voting to pick the most likely target. The classifier extends the window length dynamically if no target obtains the majority of votes. Compared with existing solutions, our classifier: (i) does not assume that any single feature extraction method will consistently outperform the others; (ii) adapts the channel selection to individual users or tasks; (iii) uses dynamic window lengths; (iv) is unsupervised (i.e., does not need training). Collectively, these characteristics make the classifier easy-to-use, especially for caregivers and others with limited technical expertise. We evaluated the performance of our classifier on a publicly available benchmark dataset from 35 healthy participants. We compared the information transfer rate (ITR) of this new classifier to those of the minimum energy combination (MEC), maximum synchronization index (MSI), and filter bank canonical correlation analysis (FBCCA). The new classifier increases average ITR to 123.5 bits-per-minute (bpm), 47.5, 51.2, and 19.5 bpm greater than the MEC, MSI, and FBCCA classifiers, respectively.

RevDate: 2021-08-24

Ali A, Liaqat S, Tariq H, et al (2021)

Neonatal calf diarrhea: A potent reservoir of multi-drug resistant bacteria, environmental contamination and public health hazard in Pakistan.

The Science of the total environment, 799:149450 pii:S0048-9697(21)04524-1 [Epub ahead of print].

Though emergence of multi-drug resistant bacteria in the environment is a demonstrated worldwide phenomenon, limited research is reported about the prevalence of resistant bacteria in fecal ecology of neonatal calf diarrhea (NCD) animals in Pakistan. The present study aimed to identify and assess the prevalence of bacterial pathogens and their resistance potential in the fecal ecology of NCD diseased animals of Pakistan. The presence of antibiotic resistance genes (blaTEM, blaNDM-1, blaCTX-M, qnrS) was also investigated. A total of 51 bacterial isolates were recovered from feces of young diarrheic animals (n = 11), collected from 7 cities of Pakistan and identified on the basis of 16S rRNA gene sequence and phylogenetic analysis. Selected isolates were subjected to antimicrobial susceptibility by disc diffusion method while polymerase chain reaction (PCR) was used to characterize the blaTEM, blaNDM-1, blaCTX-M, qnrS and mcr-1 antibiotic resistance genes. Based on the 16S rRNA gene sequences (Accession numbers: LC488898 to LC488948), all isolates were identified that belonged to seventeen genera with the highest prevalence rate for phylum Proteobacteria and genus Bacillus (23%). Antibiotic susceptibility explained the prevalence of resistance in isolates ciprofloxacin (100%), ampicillin (100%), sulfamethoxazole-trimethoprim (85%), tetracycline (75%), amoxicillin (55%), ofloxacin (50%), ceftazidime (45%), amoxicillin/clavulanic acid (45%), levofloxacin (30%), cefpodoxime (25%), cefotaxime (25%), cefotaxime/clavulanic acid (20%), and imipenem (10%). MICs demonstrated that almost 90% isolates were multi-drug resistant (against at least three antibiotics), specially against ciprofloxacin, and tetracycline with the highest resistance levels for Shigella sp. (NCCP-421) (MIC-CIP up to 75 μg mL-1) and Escherichia sp. (NCCP-432) (MIC-TET up to 250 μg mL-1). PCR-assisted detection of antibiotic resistance genes showed that 54% isolates were positive for blaTEM gene, 7% isolates were positive for blaCTX-M gene, 23% isolates were positive for each of qnrS and mcr-1 genes, 23% isolates were co-positive in combinations of qnrS and mcr-1 genes and blaTEM and mcr-1 genes, whereas none of the isolate showed presence of blaNDM-1 gene.

RevDate: 2021-08-23

Larzabal C, Bonnet S, Costecalde T, et al (2021)

Long-term stability of the chronic epidural wireless recorder WIMAGINE in tetraplegic patients.

Journal of neural engineering [Epub ahead of print].

AIMS: The long-term clinical validation of ABCDEFGH, a wireless 64-channel epidural ElectroCorticoGram (ECoG) recorder was investigated. The ABCDEFGH device was implanted in two quadriplegic patients within the context of a Brain-Computer Interface (BCI) protocol. This study focused on the ECoG signal quality and stability in two patients bilaterally implanted in June 2017 (P1) and in November 2019 (P2).

METHODS: The ECoG signal was recorded at rest prior to each BCI session resulting in a 32-month and in a 14-month follow-up for P1 and P2 respectively. State-of-the-art signal evaluation metrics such as Root Mean Square (RMS), the Band Power (BP), the Signal to Noise Ratio (SNR), the Effective Bandwidth (EBW) and the Spectral Edge Frequency (SEF) were used to evaluate stability of signal over the implantation time course. The time-frequency maps obtained from task-related motor activations were also studied to investigate the long-term selectivity of the electrodes.

RESULTS: Based on temporal linear regressions, we report a limited decrease of the RMS, BP and SNR, and a remarkable steadiness of the EBW and SEF, which is of great interest for long-term clinical applications. Time-frequency maps obtained during motor imaginary, showed a high level of discrimination one month after surgery and also after 2 years.

CONCLUSIONS: The ABCDEFGH epidural device showed high stability over time. The signal evaluation metrics of two quadriplegic patients during 32 months and 14 months respectively provide strong evidence that this wireless implant is well-suited for long-term ECoG recording.

SIGNIFICANCE: These findings are relevant for the future of implantable BCIs, and could benefit other patients with spinal cord injury, Amyotrophic Lateral Sclerosis, neuromuscular diseases or drug-resistant epilepsy.

RevDate: 2021-08-23

Sun J, He J, X Gao (2021)

Neurofeedback training of the control network improves children's performance with an SSVEP-based BCI.

Neuroscience pii:S0306-4522(21)00416-4 [Epub ahead of print].

In the past 20 years, neural engineering has made unprecedented progress in the interpretation of brain information (e.g., brain-computer interfaces) and in neuromodulation (e.g., electromagnetic stimulation and neurofeedback). However, there has been little research aiming to improve the performance of brain-computer interfaces (BCIs) using neuromodulation. The present study presents a novel design for a neurofeedback training (NFT) method to improve the operation of a steady-state visual evoked potential (SSVEP)-based BCI and further explores its underlying mechanisms. The use of NFT to upregulate alpha-band power in the user's parietal lobe is presented in this study as a new neuromodulation method to improve SSVEP-based BCI in this study. After users completed this NFT intervention, the signal-to-noise ratio (SNR), accuracy, and information transfer rate (ITR) of the SSVEP-based BCI were increased by 5.8%, 4.7%, and 15.6%, respectively. However, no improvement was observed in the control group in which the subjects did not participate in NFT. Moreover, a general reinforcement of the information flow from the parietal lobe to the occipital lobe was observed. Evidence from a network analysis and an attention test further indicates that NFT improves attention by developing the control capacity of the parietal lobe and then enhances the above SSVEP indicators. Upregulating the amplitude of parietal alpha oscillations using NFT significantly improves the SSVEP-based BCI performance by modulating the control network. The study validates an effective neuromodulation method and possibly contributes to explaining the function of the parietal lobe in the control network.

RevDate: 2021-08-23

Li MA, Han JF, JF Yang (2021)

Automatic feature extraction and fusion recognition of motor imagery EEG using multilevel multiscale CNN.

Medical & biological engineering & computing [Epub ahead of print].

A motor imagery EEG (MI-EEG) signal is often selected as the driving signal in an active brain computer interface (BCI) system, and it has been a popular field to recognize MI-EEG images via convolutional neural network (CNN), which poses a potential problem for maintaining the integrity of the time-frequency-space information in MI-EEG images and exploring the feature fusion mechanism in the CNN. However, information is excessively compressed in the present MI-EEG image, and the sequential CNN is unfavorable for the comprehensive utilization of local features. In this paper, a multidimensional MI-EEG imaging method is proposed, which is based on time-frequency analysis and the Clough-Tocher (CT) interpolation algorithm. The time-frequency matrix of each electrode is generated via continuous wavelet transform (WT), and the relevant section of frequency is extracted and divided into nine submatrices, the longitudinal sums and lengths of which are calculated along the directions of frequency and time successively to produce a 3 × 3 feature matrix for each electrode. Then, feature matrix of each electrode is interpolated to coincide with their corresponding coordinates, thereby yielding a WT-based multidimensional image, called WTMI. Meanwhile, a multilevel and multiscale feature fusion convolutional neural network (MLMSFFCNN) is designed for WTMI, which has dense information, low signal-to-noise ratio, and strong spatial distribution. Extensive experiments are conducted on the BCI Competition IV 2a and 2b datasets, and accuracies of 92.95% and 97.03% are yielded based on 10-fold cross-validation, respectively, which exceed those of the state-of-the-art imaging methods. The kappa values and p values demonstrate that our method has lower class skew and error costs. The experimental results demonstrate that WTMI can fully represent the time-frequency-space features of MI-EEG and that MLMSFFCNN is beneficial for improving the collection of multiscale features and the fusion recognition of general and abstract features for WTMI.

RevDate: 2021-08-23

Trinh TT, Tsai CF, Hsiao YT, et al (2021)

Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs.

Frontiers in computational neuroscience, 15:700467.

Individuals with mild cognitive impairment (MCI) are at high risk of developing into dementia (e. g., Alzheimer's disease, AD). A reliable and effective approach for early detection of MCI has become a critical challenge. Although compared with other costly or risky lab tests, electroencephalogram (EEG) seems to be an ideal alternative measure for early detection of MCI, searching for valid EEG features for classification between healthy controls (HCs) and individuals with MCI remains to be largely unexplored. Here, we design a novel feature extraction framework and propose that the spectral-power-based task-induced intra-subject variability extracted by this framework can be an encouraging candidate EEG feature for the early detection of MCI. In this framework, we extracted the task-induced intra-subject spectral power variability of resting-state EEGs (as measured by a between-run similarity) before and after participants performing cognitively exhausted working memory tasks as the candidate feature. The results from 74 participants (23 individuals with AD, 24 individuals with MCI, 27 HC) showed that the between-run similarity over the frontal and central scalp regions in the HC group is higher than that in the AD or MCI group. Furthermore, using a feature selection scheme and a support vector machine (SVM) classifier, the between-run similarity showed encouraging leave-one-participant-out cross-validation (LOPO-CV) classification performance for the classification between the MCI and HC (80.39%) groups and between the AD vs. HC groups (78%), and its classification performance is superior to other widely-used features such as spectral powers, coherence, and the complexity estimated by Katz's method extracted from single-run resting-state EEGs (a common approach in previous studies). The results based on LOPO-CV, therefore, suggest that the spectral-power-based task-induced intra-subject EEG variability extracted by the proposed feature extraction framework has the potential to serve as a neurophysiological feature for the early detection of MCI in individuals.

RevDate: 2021-08-23

Di Liberto GM, Marion G, SA Shamma (2021)

Accurate Decoding of Imagined and Heard Melodies.

Frontiers in neuroscience, 15:673401.

Music perception requires the human brain to process a variety of acoustic and music-related properties. Recent research used encoding models to tease apart and study the various cortical contributors to music perception. To do so, such approaches study temporal response functions that summarise the neural activity over several minutes of data. Here we tested the possibility of assessing the neural processing of individual musical units (bars) with electroencephalography (EEG). We devised a decoding methodology based on a maximum correlation metric across EEG segments (maxCorr) and used it to decode melodies from EEG based on an experiment where professional musicians listened and imagined four Bach melodies multiple times. We demonstrate here that accurate decoding of melodies in single-subjects and at the level of individual musical units is possible, both from EEG signals recorded during listening and imagination. Furthermore, we find that greater decoding accuracies are measured for the maxCorr method than for an envelope reconstruction approach based on backward temporal response functions (bTRF env). These results indicate that low-frequency neural signals encode information beyond note timing, especially with respect to low-frequency cortical signals below 1 Hz, which are shown to encode pitch-related information. Along with the theoretical implications of these results, we discuss the potential applications of this decoding methodology in the context of novel brain-computer interface solutions.

RevDate: 2021-08-21

Bhattacharyya S, Valeriani D, Cinel C, et al (2021)

Anytime collaborative brain-computer interfaces for enhancing perceptual group decision-making.

Scientific reports, 11(1):17008.

In this paper we present, and test in two realistic environments, collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of perceptual group decision-making. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to be able to provide a group decision at any time after the quickest team member casts their vote, but the quality of a cBCI-assisted decision improves monotonically the longer the group decision can wait; (2) we apply our cBCIs to two realistic scenarios of military relevance (patrolling a dark corridor and manning an outpost at night where users need to identify any unidentified characters that appear) in which decisions are based on information conveyed through video feeds; and (3) our cBCIs exploit Event-Related Potentials (ERPs) elicited in brain activity by the appearance of potential threats but, uniquely, the appearance time is estimated automatically by the system (rather than being unrealistically provided to it). As a result of these elements, in the two test environments, groups assisted by our cBCIs make both more accurate and faster decisions than when individual decisions are integrated in more traditional manners.

RevDate: 2021-08-20

Chen H, Lu F, Guo X, et al (2021)

Dimensional Analysis of Atypical Functional Connectivity of Major Depression Disorder and Bipolar Disorder.

Cerebral cortex (New York, N.Y. : 1991) pii:6355470 [Epub ahead of print].

Literatures have reported considerable heterogeneity with atypical functional connectivity (FC) pattern of psychiatric disorders. However, traditional statistical methods are hard to explore this heterogeneity pattern. We proposed a "brain dimension" method to describe the atypical FC patterns of major depressive disorder and bipolar disorder (BD). The approach was firstly applied to a simulation dataset. It was then utilized to a real resting-state functional magnetic resonance imaging dataset of 47 individuals with major depressive disorder, 32 individuals with BD, and 52 well matched health controls. Our method showed a better ability to extract the FC dimensions than traditional methods. The results of the real dataset revealed atypical FC dimensions for major depressive disorder and BD. Especially, an atypical FC dimension which exhibited decreased FC strength of thalamus and basal ganglia was found with higher severity level of individuals with BD than the ones with major depressive disorder. This study provided a novel "brain dimension" method to view the atypical FC patterns of major depressive disorder and BD and revealed shared and specific atypical FC patterns between major depressive disorder and BD.

RevDate: 2021-08-20

Han JJ (2021)

Synchron receives FDA approval to begin early feasibility study of their endovascular, brain-computer interface device.

Stentrode by Synchron is an endovascularly implanted brain computer interface platform that can relay the activities from the motor cortices of paralyzed patients, potentially offering a relatively noninvasive option for more than five million people in the United States alone.

RevDate: 2021-08-20

Quiles Pérez M, Martínez Beltrán ET, López Bernal S, et al (2021)

Breaching Subjects' Thoughts Privacy: A Study with Visual Stimuli and Brain-Computer Interfaces.

Journal of healthcare engineering, 2021:5517637.

Brain-computer interfaces (BCIs) started being used in clinical scenarios, reaching nowadays new fields such as entertainment or learning. Using BCIs, neuronal activity can be monitored for various purposes, with the study of the central nervous system response to certain stimuli being one of them, being the case of evoked potentials. However, due to the sensitivity of these data, the transmissions must be protected, with blockchain being an interesting approach to ensure the integrity of the data. This work focuses on the visual sense, and its relationship with the P300 evoked potential, where several open challenges related to the privacy of subjects' information and thoughts appear when using BCI. The first and most important challenge is whether it would be possible to extract sensitive information from evoked potentials. This aspect becomes even more challenging and dangerous if the stimuli are generated when the subject is not aware or conscious that they have occurred. There is an important gap in this regard in the literature, with only one work existing dealing with subliminal stimuli and BCI and having an unclear methodology and experiment setup. As a contribution of this paper, a series of experiments, five in total, have been created to study the impact of visual stimuli on the brain tangibly. These experiments have been applied to a heterogeneous group of ten subjects. The experiments show familiar visual stimuli and gradually reduce the sampling time of known images, from supraliminal to subliminal. The study showed that supraliminal visual stimuli produced P300 potentials about 50% of the time on average across all subjects. Reducing the sample time between images degraded the attack, while the impact of subliminal stimuli was not confirmed. Additionally, younger subjects generally presented a shorter response latency. This work corroborates that subjects' sensitive data can be extracted using visual stimuli and P300.

RevDate: 2021-08-20
CmpDate: 2021-08-20

Cikajlo I, M Pogačnik (2020)

Movement analysis of pick-and-place virtual reality exergaming in patients with Parkinson's disease.

Technology and health care : official journal of the European Society for Engineering and Medicine, 28(4):391-402.

BACKGROUND: Reduced coordination of precise small movements of the hand, wrist and fingers in Parkinson's disease (PD) has been mostly solved by medications and deep brain stimulation. The effects have been evaluated by clinical tests only.

OBJECTIVE: Virtual reality-based exergaming may enhance fine movements, decrease the medications dosage and provide an additional non-subjective evaluation.

METHODS: 3D pick-and-place task (10Cubes) has been developed in a virtual world. The person placed the virtual cubes by the virtual hand, an avatar of the real hand tracked by a Leap Motion Controller (LMC). The system computed the time of manipulating the cube, the total time, the average time, the speed, and the distance. It counted and managed the number of cubes touched, and calculated the hand shake level, i.e. the average tremor index. A pilot test was carried out in a healthy neurologically intact person and a patient with PD using a 3D head-mounted device (HMD) or LCD screen.

RESULTS: The results indicate that substantial and also statistically significant (p< 0.05) differences exist between both participants in all objective parameters; the most noteworthy is the average tremor index. However, we found the parameters only marginally different with 2D equipment.

CONCLUSIONS: The evaluation system of 10Cubes has proved applicable at an unchanged medication plan, but its clinical effectiveness could be confirmed with a randomized study.

RevDate: 2021-08-20
CmpDate: 2021-08-20

Rajan RK, M Ramanathan (2020)

Identification and neuroprotective evaluation of a potential c-Jun N-terminal kinase 3 inhibitor through structure-based virtual screening and in-vitro assay.

Journal of computer-aided molecular design, 34(6):671-682.

The c-Jun N-terminal kinase 3 (JNK3) signaling cascade is activated during cerebral ischemia leading to neuronal damage. The present study was carried out to identify and evaluate novel JNK3 inhibitors using in-silico and in-vitro approach. A total of 380 JNK3 inhibitors belonging to different organic groups was collected from the previously reported literature. These molecules were used to generate a pharmacophore model. This model was used to screen a chemical database (SPECS) to identify newer molecules with similar chemical features. The top 1000 hits molecules were then docked against the JNK3 enzyme coordinate following GLIDE rigid receptor docking (RRD) protocol. Best posed molecules of RRD were used during induced-fit docking (IFD), allowing receptor flexibility. Other computational predictions such as binding free energy, electronic configuration and ADME/tox were also calculated. Inferences from the best pharmacophore model suggested that, in order to have specific JNK3 inhibitory activity, the molecules must possess one H-bond donor, two hydrophobic and two ring features. Docking studies suggested that the main interaction between lead molecules and JNK3 enzyme consisted of hydrogen bond interaction with methionine 149 of the hinge region. It was also observed that the molecule with better MM-GBSA dG binding free energy, had greater correlation with JNK3 inhibition. Lead molecule (AJ-292-42151532) with the highest binding free energy (dG = 106.8 Kcal/mol) showed better efficacy than the SP600125 (reference JNK3 inhibitor) during cell-free JNK3 kinase assay (IC50 = 58.17 nM) and cell-based neuroprotective assay (EC50 = 7.5 µM).

RevDate: 2021-08-19

Chen S, Zhang X, Shen X, et al (2021)

Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Brain-machine interfaces (BMIs) help the disabled restore body functions by translating neural activity into digital commands to control external devices. Neural adaptation, where the brain signals change in response to external stimuli or movements, plays an important role in BMIs. When subjects purely use neural activity to brain-control a prosthesis, some neurons will actively explore a new tuning property to accomplish the movement task. The prediction of this neural tuning property can help subjects adapt more efficiently to brain control and maintain a good decoding performance. Existing prediction methods track the slow change of the tuning property in the manual control, which is not suitable for the fast neural adaptation in brain control. In order to identify the active neurons in brain control and track their tuning property changes, we propose a globally adaptive point process method (GaPP) to estimate the neural modulation state from spike trains, decompose the states into the hyper preferred direction and reconstruct the kinematics in a dual-model framework. We implement the method on real data from rats performing a two-lever discrimination task under manual control and brain control. The results show our method successfully predicts the neural modulation state and identifies the neurons that become active in brain control. Compared to the existing method, ours tracks the fast changes of the hyper preferred direction from manual control to brain control more accurately and efficiently and reconstructs the kinematics better and faster.

RevDate: 2021-08-19

Charlebois CM, Caldwell DJ, Rampersad SM, et al (2021)

Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation.

Frontiers in neuroscience, 15:691701.

Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against in vivo voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best estimated the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm (r = 0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with in vivo intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications.

RevDate: 2021-08-18

Liu J, Ye F, H Xiong (2021)

Multi-class motor imagery EEG classification method with high accuracy and low individual differences based on hybrid neural network.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Most current methods of classifying different patterns for motor imagery EEG signals require complex pre-processing and feature extraction steps, which consume time and lack adaptability, ignoring individual differences in EEG signals. It is essential to improve algorithm performance with the increased classes and diversity of subjects.

APPROACH: This study introduces deep learning method for end-to-end learning to complete the classification of four-class MI tasks, aiming to improve the recognition rate and balance the classification accuracy among different subjects. A new one-dimensional input data representation method is proposed. This representation method can increase the number of samples and ignore the influence of channel correlation. In addition, a cascade network of convolutional neural network and gated recurrent unit is designed to learn time-frequency information from EEG data without extracting features manually, this model can capture the hidden representations related to different MI mode of each people; Main results. Experiments on BCI Competition 2a dataset and actual collected dataset achieve high accuracy near 99.40% and 92.56%, and the standard deviation is 0.34 and 1.35 respectively. Results demonstrate that the proposed method outperforms the advanced methods and baseline models.

SIGNIFICANCE: Experimental results show that the proposed method improves the accuracy of multi-classification and overcomes the impact of individual differences on classification by training neural network subject-dependent, which promotes the development of actual brain-computer interface systems.

RevDate: 2021-08-18

Xu L, Xu M, Ma Z, et al (2021)

Enhancing transfer performance across datasets for brain-computer interfaces using a combination of alignment strategies and adaptive batch normalization.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Recently, transfer learning (TL) and deep learning (DL) have been introduced to solve intra- and inter-subject variability problems in Brain-Computer Interfaces (BCIs). However, current TL and DL algorithms are usually validated within a single dataset, assuming that data of the test subjects are acquired under the same condition as that of training (source) subjects. This assumption is generally violated in practice because of different acquisition systems and experimental settings across studies and datasets. Thus, the generalization ability of these algorithms needs further validations in a cross-dataset scenario, which is closer to the actual situation. This study compared the transfer performance of pre-trained deep-learning models with different preprocessing strategies in a cross-dataset scenario.

APPROACH: This study used four publicly available motor imagery (MI) datasets, each was successively selected as a source dataset, and the others were used as target datasets. EEGNet and ShallowConvNet with four preprocessing strategies, namely channel normalization, trial normalization, Euclidean alignment, and Riemannian alignment, were trained with the source dataset. The transfer performance of pre-trained models was validated on the target datasets. This study also used Adaptive Batch Normalization (AdaBN) for reducing interval covariate shift across datasets. This study compared the transfer performance of using the four preprocessing strategies and that of a baseline approach based on Manifold embedded knowledge transfer (MEKT). This study also explored the possibility and performance of fusing MEKT and EEGNet.

MAIN RESULTS: The results show that DL models with alignment strategies had significantly better transfer performance than the other two preprocessing strategies. As an unsupervised domain adaptation method, AdaBN could also significantly improve the transfer performance of DL models. The transfer performance of DL models that combined AdaBN and alignment strategies significantly outperformed MEKT. Moreover, the generalizability of EEGNet models that combined AdaBN and alignment strategies could be further improved via the domain adaptation step in MEKT, achieving the best generalization ability among multiple datasets (BNCI2014001: 0.788, PhysionetMI: 0.679, Weibo2014: 0.753, Cho2017: 0.650).

SIGNIFICANCE: The combination of alignment strategies and AdaBN could easily improve the generalizability of DL models without fine-tuning. This study may provide new insights into the design of transfer neural networks for BCIs by separating source and target batch normalization layers in the domain adaptation process.

RevDate: 2021-08-18

Peterson V, Nieto N, Wyser D, et al (2021)

Transfer Learning based on Optimal Transport for Motor Imagery Brain-Computer Interfaces.

IEEE transactions on bio-medical engineering, PP: [Epub ahead of print].

OBJECTIVE: This paper tackles the cross-sessions variability of electroencephalography-based brain-computer interfaces (BCIs) in order to avoid the lengthy recalibration step of the decoding method before every use.

METHODS: We develop a new approach of domain adaptation based on optimal transport to tackle brain signal variability between sessions of motor imagery BCIs. We propose a backward method where, unlike the original formulation, the data from a new session are transported to a calibration session, and thereby avoiding model retraining. Several domain adaptation approaches are evaluated and compared. We simulated two possible online scenarios: i) block-wise adaptation and ii) sample-wise adaptation. In this study, we collect a dataset of 10 subjects performing a hand motor imagery task in 2 sessions. A publicly available dataset is also used.

RESULTS: For the first scenario, results indicate that classifier retraining can be avoided by means of our backward formulation yielding to equivalent classification performance as compared to retraining solutions. In the second scenario, classification performance rises up to 90.23\% overall accuracy when the label of the indicated mental task is used to learn the transport. Adaptive time is between 10 and 80 times faster than the other methods.

CONCLUSIONS: The proposed method is able to mitigate the cross-session variability in motor imagery BCIs.

SIGNIFICANCE: The backward formulation is an efficient retraining-free approach built to avoid lengthy calibration times. Thus, the BCI can be actively used after just a few minutes of setup. This is important for practical applications such as BCI-based motor rehabilitation.

RevDate: 2021-08-18

Liu B, Chen X, Li X, et al (2021)

Align and pool for EEG headset domain adaptation (ALPHA) to facilitate dry electrode based SSVEP-BCI.

IEEE transactions on bio-medical engineering, PP: [Epub ahead of print].

OBJECTIVE: The steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) implemented in dry electrodes is a promising paradigm for alternative and augmentative communication in real-world applications. To improve its performance and reduce the calibration effort for dry-electrode systems, we utilize cross-device transfer learning by exploiting auxiliary individual wet-electrode electroencephalogram (EEG).

METHODS: We proposed a novel transfer learning framework named ALign and Pool for EEG Headset domain Adaptation (ALPHA), which aligns the spatial pattern and the covariance for domain adaptation. To evaluate its efficacy, 75 subjects performed an experiment of 2 sessions involving a 12-target SSVEP-BCI task.

RESULTS: ALPHA significantly outperformed a baseline approach (canonical correlation analysis, CCA) and two competing transfer learning approaches (transfer template CCA, ttCCA and least square transformation, LST) in two transferring directions. When transferring from wet to dry EEG headsets, ALPHA significantly outperformed the fully calibrated approach of task-related component analysis (TRCA).

CONCLUSION: ALPHA advances the frontier of recalibration-free cross-device transfer learning for SSVEP-BCIs and boosts the performance of dry electrode based systems.

SIGNIFICANCE: ALPHA has methodological and practical implications and pushes the boundary of dry electrode based SSVEP-BCI toward real-world applications.

RevDate: 2021-08-18

Krishnan A, Sharma G, Devana SK, et al (2021)

Urinary adenosine triphosphate and nitric oxide levels in patients with underactive bladder: a preliminary study.

World journal of urology [Epub ahead of print].

INTRODUCTION: Various in vitro and in vivo animal studies have shown that adenosine triphosphate (ATP) has a stimulatory role and nitric oxide (NO) has an inhibitory role in modulating bladder contractility. However, it is not known what happens to the urinary levels of ATP and NO in humans with underactive bladder (UAB).

METHODS: In this prospective case-control study, we compared ATP and NO levels in twenty six male patients of UAB with a bladder contractility index (BCI) of < 100 and 18 healthy male volunteers without any lower urinary tract symptoms (LUTS).

RESULTS: The mean urinary ATP levels were significantly lower in cases compared to controls (546.1 ± 37.3 pg/µl vs. 610.7 ± 24.9 pg/µl, p value < 0.001) and the mean NO levels were significantly higher in cases compared to controls (1233.4 ± 91.2 pg/µl vs. 1126.3 ± 91.3.4 pg/µl, p value < 0.001). The mean NO/ATP ratio in cases was significantly higher than that of controls (2.26 ± 0.2 vs. 1.84 ± 0.18, p value < 0.000). Using receiver operating curve (ROC) analysis, we noted the area under the curve (AUC) for NO/ATP ratio to be 0.91 in the diagnosis of cases. A cut-off value of 2.06 for NO/ATP ratio had sensitivity, specificity and diagnostic accuracy of 88.5%, 88.9% and 88.6%, respectively, in diagnosing patients with UAB.

CONCLUSION: Patients with UAB have significantly higher levels of urinary NO and decreased levels of urinary ATP. Urinary NO/ATP levels can be considered as a noninvasive alternate test for diagnosing bladder underactivity.

RevDate: 2021-08-18
CmpDate: 2021-08-18

Shah P, Chavda V, Patel S, et al (2020)

Promising Anti-stroke Signature of Voglibose: Investigation through In- Silico Molecular Docking and Virtual Screening in In-Vivo Animal Studies.

Current gene therapy, 20(3):223-235.

BACKGROUND: Postprandial hyperglycemia considered to be a major risk factor for cerebrovascular complications.

OBJECTIVE: The current study was designed to elucidate the beneficial role of voglibose via in-silico in vitro to in-vivo studies in improving the postprandial glycaemic state by protection against strokeprone type 2 diabetes.

MATERIALS AND METHODS: In-Silico molecular docking and virtual screening were carried out with the help of iGEMDOCK+ Pymol+docking software and Protein Drug Bank database (PDB). Based on the results of docking studies, in-vivo investigation was carried out for possible neuroprotective action. T2DM was induced by a single injection of streptozotocin (90mg/kg, i.v.) to neonates. Six weeks after induction, voglibose was administered at the dose of 10mg/kg p.o. for two weeks. After eight weeks, diabetic rats were subjected to middle cerebral artery occlusion, and after 72 hours of surgery, neurological deficits were determined. The blood was collected for the determination of serum glucose, CK-MB, LDH and lipid levels. Brains were excised for determination of brain infarct volume, brain hemisphere weight difference, Na+-K+ ATPase activity, ROS parameters, NO levels, and aldose reductase activity.

RESULTS: In-silico docking studies showed good docking binding score for stroke associated proteins, which possibly hypotheses neuroprotective action of voglibose in stroke. In the present in-vivo study, pre-treatment with voglibose showed a significant decrease (p<0.05) in serum glucose and lipid levels. Voglibose has shown significant (p<0.05) reduction in neurological score, brain infarct volume, the difference in brain hemisphere weight. On biochemical evaluation, treatment with voglibose produced significant (p<0.05) decrease in CK-MB, LDH, and NO levels in blood and reduction in Na+-K+ ATPase, oxidative stress, and aldose reductase activity in brain homogenate.

CONCLUSION: In-silico molecular docking and virtual screening studies and in-vivo studies in MCAo induced stroke, animal model outcomes support the strong anti-stroke signature for possible neuroprotective therapeutics.

RevDate: 2021-08-18
CmpDate: 2021-08-18

Su XH, Deng Z, He BW, et al (2020)

Haptic-based virtual reality simulator for lateral ventricle puncture operation.

The international journal of medical robotics + computer assisted surgery : MRCAS, 16(6):1-10.

BACKGROUND: The implementation of lateral ventricle puncture (LVP) operation is challenging due to the complex anatomy structure of human brains. Surgical simulator has been proved to be effective in surgical training. However, few works consider the integration of visual and haptic feedback.

METHODS: Aim at achieving a realistic haptic interaction, this paper proposes a haptic-based virtual reality (VR) simulator for the LVP operation. In this simulator, we first reconstruct the three-dimension (3D) model of human brains for tissue/instrument interaction. Then a preoperative planning method based on geometry analysis is introduced to find the feasible entry point of LVP operation. A hierarchical bounding-box collision detection approach is proposed to render haptic feedback that is transferred to humans. Finally, a set of experiments on the proposed simulator and 3D printed models of human brains is carried out.

RESULTS: Two sets of experiments are conducted to evaluate the effectiveness of the proposed haptic-based simulator: experiments in the simulator and experiments on a 3D printed brain model. The proposed simulator allows neurosurgeons to train the LVP operation by visualizing the 3D virtual human brain and feeling realistic haptic feedback.

CONCLUSIONS: We demonstrated that the proposed haptic-based VR simulator can improve the performance of the LVP operation effectively and reduce the operation time.

RevDate: 2021-08-18
CmpDate: 2021-08-18

Zhou Z, Li X, S Kleiven (2020)

Biomechanics of Periventricular Injury.

Journal of neurotrauma, 37(8):1074-1090.

Periventricular injury is frequently noted as one aspect of severe traumatic brain injury (TBI) and the presence of the ventricles has been hypothesized to be a primary pathogenesis associated with the prevalence of periventricular injury in patients with TBI. Although substantial endeavors have been made to elucidate the potential mechanism, a thorough explanation for this hypothesis appears lacking. In this study, a three-dimensional (3D) finite element (FE) model of the human head with an accurate representation of the cerebral ventricles is developed accounting for the fluid properties of the intraventricular cerebrospinal fluid (CSF) as well as its interaction with the brain. An additional model is developed by replacing the intraventricular CSF with a substitute with brain material. Both models are subjected to rotational accelerations with magnitudes suspected to induce severe diffuse axonal injury. The results reveal that the presence of the ventricles leads to increased strain in the periventricular region, providing a plausible explanation for the vulnerability of the periventricular region. In addition, the strain-exacerbation effect associated with the presence of the ventricles is also noted in the paraventricular region, although less pronounced than that in the periventricular region. The current study advances the understanding of the periventricular injury mechanism as well as the detrimental effects that the ventricles exert on the periventricular and paraventricular brain tissue.

RevDate: 2021-08-18
CmpDate: 2021-08-18

Abadia I, Naveros F, Garrido JA, et al (2021)

On Robot Compliance: A Cerebellar Control Approach.

IEEE transactions on cybernetics, 51(5):2476-2489.

The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebellar controller provides torque commands allowing for accurate and coordinated arm movements. To compute these output motor commands, the spiking cerebellar controller receives the robot's sensorial signals, the robot's goal behavior, and an instructive signal. These input signals are translated into a set of evolving spiking patterns representing univocally a specific system state at every point of time. Spike-timing-dependent plasticity (STDP) is then supported, allowing for building adaptive control. The spiking cerebellar controller continuously adapts the torque commands provided to the robot from experience as STDP is deployed. Adaptive torque commands, in turn, help the spiking cerebellar controller to cope with built-in elastic elements within the robot's actuators mimicking human muscles (inherently elastic). We propose a natural integration of a bioinspired control scheme, based on the cerebellum, with a compliant robot. We prove that our compliant approach outperforms the accuracy of the default factory-installed position control in a set of tasks used for addressing cerebellar motor behavior: controlling six degrees of freedom (DoF) in smooth movements, fast ballistic movements, and unstructured scenario compliant movements.

RevDate: 2021-08-17

Ravindran A, Rieke JD, Zapata JDA, et al (2021)

Four methods of brain pattern analyses of fMRI signals associated with wrist extension versus wrist flexion studied for potential use in future motor learning BCI.

PloS one, 16(8):e0254338 pii:PONE-D-20-16478.

OBJECTIVE: In stroke survivors, a treatment-resistant problem is inability to volitionally differentiate upper limb wrist extension versus flexion. When one intends to extend the wrist, the opposite occurs, wrist flexion, rendering the limb non-functional. Conventional therapeutic approaches have had limited success in achieving functional recovery of patients with chronic and severe upper extremity impairments. Functional magnetic resonance imaging (fMRI) neurofeedback is an emerging strategy that has shown potential for stroke rehabilitation. There is a lack of information regarding unique blood-oxygenation-level dependent (BOLD) cortical activations uniquely controlling execution of wrist extension versus uniquely controlling wrist flexion. Therefore, a first step in providing accurate neural feedback and training to the stroke survivor is to determine the feasibility of classifying (or differentiating) brain activity uniquely associated with wrist extension from that of wrist flexion, first in healthy adults.

APPROACH: We studied brain signal of 10 healthy adults, who performed wrist extension and wrist flexion during fMRI data acquisition. We selected four types of analyses to study the feasibility of differentiating brain signal driving wrist extension versus wrist flexion, as follows: 1) general linear model (GLM) analysis; 2) support vector machine (SVM) classification; 3) 'Winner Take All'; and 4) Relative Dominance.

RESULTS: With these four methods and our data, we found that few voxels were uniquely active during either wrist extension or wrist flexion. SVM resulted in only minimal classification accuracies. There was no significant difference in activation magnitude between wrist extension versus flexion; however, clusters of voxels showed extension signal > flexion signal and other clusters vice versa. Spatial patterns of activation differed among subjects.

SIGNIFICANCE: We encountered a number of obstacles to obtaining clear group results in healthy adults. These obstacles included the following: high variability across healthy adults in all measures studied; close proximity of uniquely active voxels to voxels that were common to both the extension and flexion movements; in general, higher magnitude of signal for the voxels common to both movements versus the magnitude of any given uniquely active voxel for one type of movement. Our results indicate that greater precision in imaging will be required to develop a truly effective method for differentiating wrist extension versus wrist flexion from fMRI data.

RevDate: 2021-08-16

Lashgari E, U Maoz (2021)

Dimensionality reduction for classification of object weight from electromyography.

PloS one, 16(8):e0255926 pii:PONE-D-20-02285.

Electromyography (EMG) is a simple, non-invasive, and cost-effective technology for measuring muscle activity. However, multi-muscle EMG is also a noisy, complex, and high-dimensional signal. It has nevertheless been widely used in a host of human-machine-interface applications (electrical wheelchairs, virtual computer mice, prosthesis, robotic fingers, etc.) and, in particular, to measure the reach-and-grasp motions of the human hand. Here, we developed an automated pipeline to predict object weight in a reach-grasp-lift task from an open dataset, relying only on EMG data. In doing so, we shifted the focus from manual feature-engineering to automated feature-extraction by using pre-processed EMG signals and thus letting the algorithms select the features. We further compared intrinsic EMG features, derived from several dimensionality-reduction methods, and then ran several classification algorithms on these low-dimensional representations. We found that the Laplacian Eigenmap algorithm generally outperformed other dimensionality-reduction methods. What is more, optimal classification accuracy was achieved using a combination of Laplacian Eigenmaps (simple-minded) and k-Nearest Neighbors (88% F1 score for 3-way classification). Our results, using EMG alone, are comparable to other researchers', who used EMG and EEG together, in the literature. A running-window analysis further suggests that our method captures information in the EMG signal quickly and remains stable throughout the time that subjects grasp and move the object.

RevDate: 2021-08-16

de Kerckhove D (2021)

The personal digital twin, ethical considerations.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 379(2207):20200367.

The personal digital twin extends to individual persons, a concept that originated in engineering to twin complex machines with a digital simulation containing a model of its functions to monitor its past and present behaviour, and repair, correct, improve or otherwise ensure its optimal operation. Several independent trends in technological developments are seen to converge towards the elaboration of the digital replication of individual human data and life history, notably in health industries. Among the main ones, we consider the ubiquitous distribution of digital assistants, the rapid progress of machine learning concurrent with the exponential growth of 'personal' Big Data and the incipient interest in developing lifelogs. The core hypothesis here is that among the psychological effects of the digital transformation, the externalization of cognitive faculties such as memory, planning and judgement, the decision-making processes located within the human person are also emigrating to digital functions, perhaps as a prelude to a later re-integration within the person via brain-computer interfaces. The paper concludes with ethical considerations about these ongoing developments. This article is part of the theme issue 'Towards symbiotic autonomous systems'.

RevDate: 2021-08-16

Wickramaratne SD, MS Mahmud (2021)

Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data.

Frontiers in big data, 4:659146 pii:659146.

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique used for mapping the functioning human cortex. fNIRS can be widely used in population studies due to the technology's economic, non-invasive, and portable nature. fNIRS can be used for task classification, a crucial part of functioning with Brain-Computer Interfaces (BCIs). fNIRS data are multidimensional and complex, making them ideal for deep learning algorithms for classification. Deep Learning classifiers typically need a large amount of data to be appropriately trained without over-fitting. Generative networks can be used in such cases where a substantial amount of data is required. Still, the collection is complex due to various constraints. Conditional Generative Adversarial Networks (CGAN) can generate artificial samples of a specific category to improve the accuracy of the deep learning classifier when the sample size is insufficient. The proposed system uses a CGAN with a CNN classifier to enhance the accuracy through data augmentation. The system can determine whether the subject's task is a Left Finger Tap, Right Finger Tap, or Foot Tap based on the fNIRS data patterns. The authors obtained a task classification accuracy of 96.67% for the CGAN-CNN combination.

RevDate: 2021-08-16

Ni T, Ni Y, Xue J, et al (2021)

A Domain Adaptation Sparse Representation Classifier for Cross-Domain Electroencephalogram-Based Emotion Classification.

Frontiers in psychology, 12:721266.

The brain-computer interface (BCI) interprets the physiological information of the human brain in the process of consciousness activity. It builds a direct information transmission channel between the brain and the outside world. As the most common non-invasive BCI modality, electroencephalogram (EEG) plays an important role in the emotion recognition of BCI; however, due to the individual variability and non-stationary of EEG signals, the construction of EEG-based emotion classifiers for different subjects, different sessions, and different devices is an important research direction. Domain adaptation utilizes data or knowledge from more than one domain and focuses on transferring knowledge from the source domain (SD) to the target domain (TD), in which the EEG data may be collected from different subjects, sessions, or devices. In this study, a new domain adaptation sparse representation classifier (DASRC) is proposed to address the cross-domain EEG-based emotion classification. To reduce the differences in domain distribution, the local information preserved criterion is exploited to project the samples from SD and TD into a shared subspace. A common domain-invariant dictionary is learned in the projection subspace so that an inherent connection can be built between SD and TD. In addition, both principal component analysis (PCA) and Fisher criteria are exploited to promote the recognition ability of the learned dictionary. Besides, an optimization method is proposed to alternatively update the subspace and dictionary learning. The comparison of CSFDDL shows the feasibility and competitive performance for cross-subject and cross-dataset EEG-based emotion classification problems.

RevDate: 2021-08-13

Yu LM, Bafadhel M, Dorward J, et al (2021)

Inhaled budesonide for COVID-19 in people at high risk of complications in the community in the UK (PRINCIPLE): a randomised, controlled, open-label, adaptive platform trial.

Lancet (London, England) pii:S0140-6736(21)01744-X [Epub ahead of print].

BACKGROUND: A previous efficacy trial found benefit from inhaled budesonide for COVID-19 in patients not admitted to hospital, but effectiveness in high-risk individuals is unknown. We aimed to establish whether inhaled budesonide reduces time to recovery and COVID-19-related hospital admissions or deaths among people at high risk of complications in the community.

METHODS: PRINCIPLE is a multicentre, open-label, multi-arm, randomised, controlled, adaptive platform trial done remotely from a central trial site and at primary care centres in the UK. Eligible participants were aged 65 years or older or 50 years or older with comorbidities, and unwell for up to 14 days with suspected COVID-19 but not admitted to hospital. Participants were randomly assigned to usual care, usual care plus inhaled budesonide (800 μg twice daily for 14 days), or usual care plus other interventions, and followed up for 28 days. Participants were aware of group assignment. The coprimary endpoints are time to first self-reported recovery and hospital admission or death related to COVID-19, within 28 days, analysed using Bayesian models. The primary analysis population included all eligible SARS-CoV-2-positive participants randomly assigned to budesonide, usual care, and other interventions, from the start of the platform trial until the budesonide group was closed. This trial is registered at the ISRCTN registry (ISRCTN86534580) and is ongoing.

FINDINGS: The trial began enrolment on April 2, 2020, with randomisation to budesonide from Nov 27, 2020, until March 31, 2021, when the prespecified time to recovery superiority criterion was met. 4700 participants were randomly assigned to budesonide (n=1073), usual care alone (n=1988), or other treatments (n=1639). The primary analysis model includes 2530 SARS-CoV-2-positive participants, with 787 in the budesonide group, 1069 in the usual care group, and 974 receiving other treatments. There was a benefit in time to first self-reported recovery of an estimated 2·94 days (95% Bayesian credible interval [BCI] 1·19 to 5·12) in the budesonide group versus the usual care group (11·8 days [95% BCI 10·0 to 14·1] vs 14·7 days [12·3 to 18·0]; hazard ratio 1·21 [95% BCI 1·08 to 1·36]), with a probability of superiority greater than 0·999, meeting the prespecified superiority threshold of 0·99. For the hospital admission or death outcome, the estimated rate was 6·8% (95% BCI 4·1 to 10·2) in the budesonide group versus 8·8% (5·5 to 12·7) in the usual care group (estimated absolute difference 2·0% [95% BCI -0·2 to 4·5]; odds ratio 0·75 [95% BCI 0·55 to 1·03]), with a probability of superiority 0·963, below the prespecified superiority threshold of 0·975. Two participants in the budesonide group and four in the usual care group had serious adverse events (hospital admissions unrelated to COVID-19).

INTERPRETATION: Inhaled budesonide improves time to recovery, with a chance of also reducing hospital admissions or deaths (although our results did not meet the superiority threshold), in people with COVID-19 in the community who are at higher risk of complications.

FUNDING: National Institute of Health Research and United Kingdom Research Innovation.

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In the early 1990's, Robert Robbins was a faculty member at Johns Hopkins, where he directed the informatics core of GDB — the human gene-mapping database of the international human genome project. To share papers with colleagues around the world, he set up a small paper-sharing section on his personal web page. This small project evolved into The Electronic Scholarly Publishing Project.

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Rajesh Rao has written the perfect introduction to the exciting world of brain-computer interfaces. The book is remarkably comprehensive — not only including full descriptions of classic and current experiments but also covering essential background concepts, from the brain to Bayes and back. Brain-Computer Interfacing will be welcomed by a wide range of intelligent readers interested in understanding the first steps toward the symbiotic merger of brains and computers. Eberhard E. Fetz, UW

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Along with papers on classical genetics, ESP offers a collection of full-text digital books, including many works by Darwin (and even a collection of poetry — Chicago Poems by Carl Sandburg).

Timelines

ESP now offers a much improved and expanded collection of timelines, designed to give the user choice over subject matter and dates.

Biographies

Biographical information about many key scientists.

Selected Bibliographies

Bibliographies on several topics of potential interest to the ESP community are now being automatically maintained and generated on the ESP site.

ESP Picks from Around the Web (updated 07 JUL 2018 )