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Bibliography on: Brain-Computer Interface

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ESP: PubMed Auto Bibliography 02 Dec 2023 at 01:39 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 OR brain-machine OR mind-machine OR neural-control interface) NOT 26799652[PMID] ) NOT pmcbook NOT ispreviousversion

Citations The Papers (from PubMed®)


RevDate: 2023-11-29

Zhang R, Pan S, Zheng S, et al (2023)

Lipid-anchored proteasomes control membrane protein homeostasis.

Science advances, 9(48):eadj4605.

Protein degradation in eukaryotic cells is mainly carried out by the 26S proteasome, a macromolecular complex not only present in the cytosol and nucleus but also associated with various membranes. How proteasomes are anchored to the membrane and the biological meaning thereof have been largely unknown in higher organisms. Here, we show that N-myristoylation of the Rpt2 subunit is a general mechanism for proteasome-membrane interaction. Loss of this modification in the Rpt2-G2A mutant cells leads to profound changes in the membrane-associated proteome, perturbs the endomembrane system, and undermines critical cellular processes such as cell adhesion, endoplasmic reticulum-associated degradation and membrane protein trafficking. Rpt2[G2A/G2A] homozygous mutation is embryonic lethal in mice and is sufficient to abolish tumor growth in a nude mice xenograft model. These findings have defined an evolutionarily conserved mechanism for maintaining membrane protein homeostasis and underscored the significance of compartmentalized protein degradation by myristoyl-anchored proteasomes in health and disease.

RevDate: 2023-11-29

Brannigan JFM, Fry A, Opie NL, et al (2023)

Endovascular Brain-Computer Interfaces in Poststroke Paralysis.

Stroke [Epub ahead of print].

Stroke is a leading cause of paralysis, most frequently affecting the upper limbs and vocal folds. Despite recent advances in care, stroke recovery invariably reaches a plateau, after which there are permanent neurological impairments. Implantable brain-computer interface devices offer the potential to bypass permanent neurological lesions. They function by (1) recording neural activity, (2) decoding the neural signal occurring in response to volitional motor intentions, and (3) generating digital control signals that may be used to control external devices. While brain-computer interface technology has the potential to revolutionize neurological care, clinical translation has been limited. Endovascular arrays present a novel form of minimally invasive brain-computer interface devices that have been deployed in human subjects during early feasibility studies. This article provides an overview of endovascular brain-computer interface devices and critically evaluates the patient with stroke as an implant candidate. Future opportunities are mapped, along with the challenges arising when decoding neural activity following infarction. Limitations arise when considering intracerebral hemorrhage and motor cortex lesions; however, future directions are outlined that aim to address these challenges.

RevDate: 2023-11-28

Wang R, Zhou T, Li Z, et al (2023)

Using oscillatory and aperiodic neural activity features for identifying idle state in SSVEP-based BCIs reduces false triggers.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: In existing studies, rhythmic (oscillatory) components were used as main features to identify brain states, such as control and idle states, while non-rhythmic (aperiodic) components were ignored. Recent studies have shown that aperiodic (1/f) activity is functionally related to cognitive processes. It is not clear if aperiodic activity can distinguish brain states in asynchronous brain-computer interfaces (BCIs) to reduce false triggers. In this paper, we propose an asynchronous method based on the fusion of oscillatory and aperiodic features for steady-state visual evoked potential (SSVEP)-based BCIs.

METHODS: The proposed method first evaluates the oscillatory and aperiodic components of control and idle states using irregular-resampling auto-spectral analysis (IRASA). Oscillatory features are then extracted using the spectral power of the fundamental, second-harmonic, and third-harmonic frequencies of the oscillatory component, and aperiodic features are extracted using the slope and intercept of the first-order polynomial of the spectral fit of the aperiodic component under a log-logarithmic axis. The process produces two types of feature pools (oscillatory and aperiodic features). Next, feature selection (dimensionality reduction) is applied to the feature pools using Bonferroni-corrected p-values from a two-way analysis of variance (ANOVA). Last, these spatial-specific statistically significant features are used as input for classification to identify the idle state.

RESULTS: On a 7-target dataset from 15 subjects, the mix of oscillatory and aperiodic features achieved an average accuracy of 88.39% compared to 83.53% when using oscillatory features alone (4.86% improvement). The results demonstrated that the proposed idle state recognition method achieved enhanced performance by incorporating aperiodic features.

CONCLUSIONS: Our results demonstrated that (1) aperiodic features were effective in recognizing idle states and (2) fusing features of oscillatory and aperiodic components enhanced classification performance by 4.86% compared to oscillatory features alone.

RevDate: 2023-11-28

Ahmadipour P, Sani OG, Pesaran B, et al (2023)

Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales.

APPROACH: Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient learning for modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical subspace identification method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and spike-LFP population activity recorded during a naturalistic reach and grasp behavior.

RESULTS: We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower training time while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity and behavior.

SIGNIFICANCE: Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest, such as for online adaptive BMIs to track non-stationary dynamics or for reducing offline training time in neuroscience investigations.

RevDate: 2023-11-28

Liu M, Jiang N, Qin C, et al (2023)

Multimodal spatiotemporal monitoring of basal stem cell-derived organoids reveals progression of olfactory dysfunction in Alzheimer's disease.

Biosensors & bioelectronics, 246:115832 pii:S0956-5663(23)00774-1 [Epub ahead of print].

Olfactory dysfunction (OD) is a highly prevalent symptom and an early sign of neurodegenerative diseases in humans. However, the roles of peripheral olfactory system in disease progression and the mechanisms behind neurodegeneration remain to be studied. Olfactory epithelium (OE) organoid is an ideal model to study pathophysiology in vitro, yet the reliance on 3D culture condition limits continual in situ monitoring of organoid development. Here, we combined impedance biosensors and live imaging for real-time spatiotemporal analysis of OE organoids morphological and physiological features during Alzheimer's disease (AD) progression. The impedance measurements showed that organoids generated from basal stem cells of APP/PS1 transgenic mice had lower proliferation rate than that from wild-type mice. In concert with the biosensor measurements, live imaging enabled to visualize the spatial and temporal dynamics of organoid morphology. Abnormal protein aggregation and accumulation, including amyloid plaques and neurofibrillary tangles, was found in AD organoids and increased as disease progressed. This multimodal in situ bioelectrical measurement and imaging provide a new platform for investigating onset mechanisms of OD, which would shed new light on early diagnosis and treatment of neurodegenerative disease.

RevDate: 2023-11-29

Li R, Zhao X, Wang Z, et al (2023)

A Novel Hybrid Brain-Computer Interface Combining the Illusion-induced VEP and SSVEP.

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

Traditional single-modality brain-computer interface (BCI) systems are limited by their reliance on a single characteristic of brain signals. To address this issue, incorporating multiple features from EEG signals can provide robust information to enhance BCI performance. In this study, we designed and implemented a novel hybrid paradigm that combined illusion-induced visual evoked potential (IVEP) and steady-state visual evoked potential (SSVEP) with the aim of leveraging their features simultaneously to improve system efficiency. The proposed paradigm was validated by two experimental studies, which encompassed feature analysis of IVEP with a static paradigm, and performance evaluation of hybrid paradigm in comparison with the conventional SSVEP paradigm. The characteristic analysis yielded significant differences in response waveforms among different motion illusions. The performance evaluation of the hybrid BCI demonstrates the advantage of integrating illusory stimuli into the SSVEP paradigm. This integration effectively enhanced the spatio-temporal features of EEG signals, resulting in higher classification accuracy and information transfer rate (ITR) within a short time window when compared to traditional SSVEP-BCI in four-command task. Furthermore, the questionnaire results of subjective estimation revealed that proposed hybrid BCI offers less eye fatigue, and potentially higher levels of concentration, physical condition, and mental condition for users. This work first introduced the IVEP signals in hybrid BCI system that could enhance performance efficiently, which is promising to fulfill the requirements for efficiency in practical BCI control systems.

RevDate: 2023-11-28

Wu X, Liu Y, Wang X, et al (2023)

Developmental Impairments of Synaptic Refinement in the Thalamus of a Mouse Model of Fragile X Syndrome.

Neuroscience bulletin [Epub ahead of print].

While somatosensory over-reactivity is a common feature of autism spectrum disorders such as fragile X syndrome (FXS), the thalamic mechanisms underlying this remain unclear. Here, we found that the developmental elimination of synapses formed between the principal nucleus of V (PrV) and the ventral posterior medial nucleus (VPm) of the somatosensory system was delayed in fragile X mental retardation 1 gene knockout (Fmr1 KO) mice, while the developmental strengthening of these synapses was disrupted. Immunohistochemistry showed excessive VGluT2 puncta in mutants at P12-13, but not at P7-8 or P15-16, confirming a delay in somatic pruning of PrV-VPm synapses. Impaired synaptic function was associated with a reduction in the frequency of quantal AMPA events, as well as developmental deficits in presynaptic vesicle size and density. Our results uncovered the developmental impairment of thalamic relay synapses in Fmr1 KO mice and suggest that a thalamic contribution to the somatosensory over-reactivity in FXS should be considered.

RevDate: 2023-11-29

Anton NE, Ziliak MC, D Stefanidis (2023)

Augmenting mental imagery for robotic surgery using neurofeedback: results of a randomized controlled trial.

Global surgical education : journal of the Association for Surgical Education, 2(1):62.

BACKGROUND: Mental imagery (MI) can enhance surgical skills. Research has shown that through brain-computer interface (BCI), it is possible to provide feedback on MI strength. We hypothesized that adding BCI to MI training would enhance robotic skill acquisition compared with controls.

METHODS: Surgical novices were recruited. At baseline, participants completed the Mental Imagery Questionnaire (MIQ) and the Vandenburg Mental Rotation Test (MRT). Students also performed several tasks on a robotic simulator. Participants were stratified based on MIQ and robotic skill and randomized into three groups: controls, MI, and MI and BCI training. All participants completed five 2-h training sessions. One hour was devoted to practicing robotic skill on the simulator. Additionally, controls completed crosswords for one hour, the MI group completed MI training and crosswords for one hour, and the MI + BCI group completed MI training and MI-related BCI training. Following training, participants completed the same baseline assessments. A Kruskal-Wallis test was used to determine differences between groups. Mann-Whitney U tests were performed to determine specific differences between groups.

RESULTS: Twenty-seven undergraduates participated. There were post-test differences on the MRT and knot tying task. Sub-analyses revealed that the MI + BCI group significantly outperformed the other groups on knot tying. There were no appreciable differences between the control and MI groups on any measures.

CONCLUSIONS: Augmenting MI training with BCI led to significantly enhanced MI and robotic skill acquisition than traditional MI or robotic training methods. To optimize surgical skill acquisition in robotic and other surgical skills curricula, educators should consider utilizing MI with BCI training.

RevDate: 2023-11-28

Wei Q, Yu H, Wang PS, et al (2023)

Biallelic variants in the COQ4 gene caused hereditary spastic paraplegia predominant phenotype.

CNS neuroscience & therapeutics [Epub ahead of print].

INTRODUCTION: Hereditary spastic paraplegias (HSPs) comprise a group of neurodegenerative disorders characterized by progressive degeneration of upper motor neurons. Homozygous or compound heterozygous variants in COQ4 have been reported to cause primary CoQ10 deficiency-7 (COQ10D7), which is a mitochondrial disease.

AIMS: We aimed to screened COQ4 variants in a cohort of HSP patients.

METHODS: A total of 87 genetically unidentified HSP index patients and their available family members were recruited. Whole exome sequencing (WES) was performed in all probands. Functional studies were performed to identify the pathogenicity of those uncertain significance variants.

RESULTS: In this study, five different COQ4 variants were identified in three Chinese HSP pedigrees and two variants were novel, c.87dupT (p.Arg30*), c.304C>T (p.Arg102Cys). More importantly, we firstly described two early-onset pure HSP caused by COQ4 variants. Functional studies in patient-derived fibroblast lines revealed a reduction cellular CoQ10 levels and the abnormal mitochondrial structure.

CONCLUSIONS: Our findings revealed that bilateral variants in the COQ4 gene caused HSP predominant phenotype, expanding the phenotypic spectrum of the COQ4-related disorders.

RevDate: 2023-11-28

Lei A, Yu H, Lu S, et al (2023)

A second-generation M1-polarized CAR macrophage with antitumor efficacy.

Nature immunology [Epub ahead of print].

Chimeric antigen receptor (CAR) T cell therapies have successfully treated hematological malignancies. Macrophages have also gained attention as an immunotherapy owing to their immunomodulatory capacity and ability to infiltrate solid tumors and phagocytize tumor cells. The first-generation CD3ζ-based CAR-macrophages could phagocytose tumor cells in an antigen-dependent manner. Here we engineered induced pluripotent stem cell-derived macrophages (iMACs) with toll-like receptor 4 intracellular toll/IL-1R (TIR) domain-containing CARs resulting in a markedly enhanced antitumor effect over first-generation CAR-macrophages. Moreover, the design of a tandem CD3ζ-TIR dual signaling CAR endows iMACs with both target engulfment capacity and antigen-dependent M1 polarization and M2 resistance in a nuclear factor kappa B (NF-κB)-dependent manner, as well as the capacity to modulate the tumor microenvironment. We also outline a mechanism of tumor cell elimination by CAR-induced efferocytosis against tumor cell apoptotic bodies. Taken together, we provide a second-generation CAR-iMAC with an ability for orthogonal phagocytosis and polarization and superior antitumor functions in treating solid tumors relative to first-generation CAR-macrophages.

RevDate: 2023-11-27

Han J, Gu X, Yang GZ, et al (2023)

Noise-Factorized Disentangled Representation Learning for Generalizable Motor Imagery EEG Classification.

IEEE journal of biomedical and health informatics, PP: [Epub ahead of print].

MotorImagery(MI)Electroencephalography(EEG) is one of the most common Brain-Computer Interface (BCI) paradigms that has been widely used in neural rehabilitation and gaming. Although considerable research efforts have been dedicated to developing MI EEG classification algorithms, they are mostly limited in handling scenarios where the training and testing data are not from the same subject or session. Such poor generalization capability significantly limits the realization of BCI in real-world applications. In this paper, we proposed a novel framework to disentangle the representation of raw EEG data into three components, subject/session-specific, MI-taskspecific, and random noises, so that the subject/session-specific feature extends the generalization capability of the system. This is realized by a joint discriminative and generative framework, supported by a series of fundamental training losses and training strategies. We evaluated our framework on three public MI EEG datasets, and detailed experimental results show that our method can achieve superior performance by a large margin compared to current state-of-the-art benchmark algorithms.

RevDate: 2023-11-27

Ohkubo M (2023)

The emergence of non-cryogenic quantum magnetic sensors: Synergistic advancement in magnetography together with SQUID.

The Review of scientific instruments, 94(11):.

Emerging non-superconductor quantum magnetic sensors, such as optically pumped magnetometer, fluxgate, magnetic tunnel junction, and diamond nitrogen-vacancy center, are approaching the performance of superconductor quantum interference devices (SQUIDs). These sensors are enabling magnetography for human bodies and brain-computer interface. Will they completely replace the SQUID magnetography in the near future?

RevDate: 2023-11-26

Bordes A, El Bendary Y, Goudard G, et al (2023)

Benefits of vagus nerve stimulation on psychomotor functions in patients with severe drug-resistant epilepsy.

Epilepsy research, 198:107260 pii:S0920-1211(23)00185-7 [Epub ahead of print].

PURPOSE: Patients with severe drug-resistant epilepsy (DRE) experience psychomotor disorders. Our study aimed to assess the psychomotor outcomes after vagus nerve stimulation (VNS) in this population.

METHODS: We prospectively evaluated psychomotor function in 17 adult patients with severe DRE who were referred for VNS. Psychomotor functions were examined, in the preoperative period and at 18 months post-surgery, by a psychomotor therapist using a full set of the following specific tests: the Rey-Osterrieth complex figure (ROCF) test, the Zazzo's cancelation task (ZCT), the Piaget-Head test and the paired images test.

RESULTS: At 18 months post-VNS surgery, the Piaget-head scores increased by 3 points (p = 0.008) compared to baseline. Performances were also improved for ROCF test both in copy (+2.4 points, p = 0.001) and recall (+2.0 points, p = 0.008) tasks and for the paired images test (accuracy index: +28.6 %, p = 0.03). Regarding the ZCT findings, the efficiency index increased in both single (+16 %, p = 0.005) and dual (+17.1 %, p < 0.001) tasks. QoL improved in 88.2 % of patients.

CONCLUSIONS: Patients with severe DRE treated with VNS experienced improved performance in terms of global psychomotor functions. Perceptual organization, visuospatial memory, laterality awareness, sustained attention, concentration, visual scanning, and inhibition were significantly improved.

RevDate: 2023-11-28
CmpDate: 2023-11-28

Tian C (2023)

Research on Brain Signals Classification Based on Deep Learning.

Studies in health technology and informatics, 308:381-388.

With the continuous expansion of brain-computer communication, the precise identification of brain signals has become an essential task for brain-computer equipment. However, existing classification methods are primarily concentrated on the extraction features of brain signals and obtain unacceptable performance when directly used the model to a new brain signals data, which is caused by the different people has extraordinary brain signals. In this work, we utilize the deep learning methods not only extract the features of brain signals but also learn the order information of brain signals, which can satisfy the universal brain signals. Indeed, we utilize the classification features dimension distance loss function to optimize the proposed model and enhance the classification accuracy and we compare our model with existing classification methods to evaluate proposed model. From our extensive experimental results and analysis, we can conclude that our model can achieve the classification of brain signals with the reasonable accuracy and acceptable costs.

RevDate: 2023-11-28
CmpDate: 2023-11-28

He R (2023)

Perspective of Signal Processing-Based on Brain-Computer Interfaces Using Machine Learning Methods.

Studies in health technology and informatics, 308:295-302.

The application of artificial intelligence (AI) algorithms is an indispensable portion of developing brain-computer interfaces (BCI). With the continuous development of AI concepts and related technologies. AI algorithms such as neural networks play an increasingly powerful and extensive role in brain-computer interfaces. However, brain-computer interfaces are still facing many technical challenges. Due to the limitations of AI algorithms, brain-computer interfaces not only work with limited accuracy, but also can only be applied to certain simple scenarios. In order to explore the future directions and improvements of AI algorithms in the area of brain-computer interfaces, this paper will review and analyse the advanced applications of AI algorithms in the field of brain-computer interfaces in recent years and give possible future enhancements and development directions for the controversial parts of them. This review first presents the effects of different AI algorithms in BCI applications. A multi-objective classification method is compared with evolutionary algorithms in feature extraction of data. Then, a kind of supervised learning algorithm based on Event Related Potential (ERP) tags is presented to achieve a high accuracy in the process of pattern recognition. Finally, as an important experimental paradigm for BCI, a combined TFD-PSR-CSP feature extraction method, is explained for the problem of motor imagery. The "Discussion" part comprehensively analyses the advantages and disadvantages of the above algorithms and proposes a deep learning-based artificial intelligence algorithm in order to solve the problems arising from the above algorithms.

RevDate: 2023-11-28
CmpDate: 2023-11-28

Li Y (2023)

CNN-Based Image Analysis for EEG Signal Characterization.

Studies in health technology and informatics, 308:20-30.

This article focuses on an attempt to classify and recognize the characterized images of EEG signals directly. For EEG signals, the recognition and judgment of different signals has been the key direction of research. CNN (Convolutional Neural Network) models are usually used for recognition of EEG raw signals about movement and Imagery Dataset. However, the images of EEG raw signals are basically unreadable for researchers, so characterization is a common tool. However, direct recognition of the characterized images is a relatively empty area in the existing research because it requires much higher machine performance than the traditional raw signal recognition. However, feeding the extracted feature images into a CNN and training them can be an efficient and intuitive response to the potential of EEG for brain mapping. The main goal of this research is to examine the discriminative capabilities of traditional visual and image neural networks for pictures described by EEG data. This is not typical in contemporary brain-computer interface research. The direct recognition of the described photos uses a lot of GPU (graphics computing unit) resources, but for the characterized images are easier for people to read than the original images. This work indicates the viability of direct research on defined pictures and increases the application scenario of EEG signals.

RevDate: 2023-11-29

Chen A, Hao S, Han Y, et al (2023)

Exploring the effects of different BCI-based attention training games on the brain: A functional near-infrared spectroscopy study.

Neuroscience letters, 818:137567 pii:S0304-3940(23)00526-8 [Epub ahead of print].

BCI games have been widely employed as non-invasive therapeutic interventions for conditions, but their efficacy remains a subject of debate. This study explores the efficacy of two prevalent forms of Brain-Computer Interface (BCI)-based attention training games: video games (VG) and physical games (PG). The effectiveness of these games has been examined through the lens of neuroscience, using functional Near-Infrared Spectroscopy (fNIRS) to monitor cortical activation. After the fNIRS test, subjects completed an Intrinsic Motivation Inventory (IMI) questionnaire. PG tasks activated six channels (L-PFC, R-PFC and R-TL), while VG tasks activated only one (R-PFC). Furthermore, females exhibited stronger activation during PG tasks, while males had none in either. Our findings suggest that under equivalent game rules and themes, PG may prove more effective for cognitive rehabilitation than VG, with stronger intrinsic motivation. We also found this result may exhibit gender differences. Finally, this research offers valuable insights for the future design of BCI-based games from a neuroscience perspective.

RevDate: 2023-11-25

Wang X, Wang Y, Qi W, et al (2023)

BrainGridNet: A two-branch depthwise CNN for decoding EEG-based multi-class motor imagery.

Neural networks : the official journal of the International Neural Network Society, 170:312-324 pii:S0893-6080(23)00660-3 [Epub ahead of print].

Brain-computer interfaces (BCIs) based on motor imagery (MI) enable the disabled to interact with the world through brain signals. To meet demands of real-time, stable, and diverse interactions, it is crucial to develop lightweight networks that can accurately and reliably decode multi-class MI tasks. In this paper, we introduce BrainGridNet, a convolutional neural network (CNN) framework that integrates two intersecting depthwise CNN branches with 3D electroencephalography (EEG) data to decode a five-class MI task. The BrainGridNet attains competitive results in both the time and frequency domains, with superior performance in the frequency domain. As a result, an accuracy of 80.26 percent and a kappa value of 0.753 are achieved by BrainGridNet, surpassing the state-of-the-art (SOTA) model. Additionally, BrainGridNet shows optimal computational efficiency, excels in decoding the most challenging subject, and maintains robust accuracy despite the random loss of 16 electrode signals. Finally, the visualizations demonstrate that BrainGridNet learns discriminative features and identifies critical brain regions and frequency bands corresponding to each MI class. The convergence of BrainGridNet's strong feature extraction capability, high decoding accuracy, steady decoding efficacy, and low computational costs renders it an appealing choice for facilitating the development of BCIs.

RevDate: 2023-11-27

Wolf P, T Götzelmann (2023)

VEPdgets: Towards Richer Interaction Elements Based on Visually Evoked Potentials.

Sensors (Basel, Switzerland), 23(22):.

For brain-computer interfaces, a variety of technologies and applications already exist. However, current approaches use visual evoked potentials (VEP) only as action triggers or in combination with other input technologies. This paper shows that the losing visually evoked potentials after looking away from a stimulus is a reliable temporal parameter. The associated latency can be used to control time-varying variables using the VEP. In this context, we introduced VEP interaction elements (VEP widgets) for a value input of numbers, which can be applied in various ways and is purely based on VEP technology. We carried out a user study in a desktop as well as in a virtual reality setting. The results for both settings showed that the temporal control approach using latency correction could be applied to the input of values using the proposed VEP widgets. Even though value input is not very accurate under untrained conditions, users could input numerical values. Our concept of applying latency correction to VEP widgets is not limited to the input of numbers.

RevDate: 2023-11-27
CmpDate: 2023-11-27

Farabbi A, L Mainardi (2023)

Domain-Specific Processing Stage for Estimating Single-Trail Evoked Potential Improves CNN Performance in Detecting Error Potential.

Sensors (Basel, Switzerland), 23(22):.

We present a novel architecture designed to enhance the detection of Error Potential (ErrP) signals during ErrP stimulation tasks. In the context of predicting ErrP presence, conventional Convolutional Neural Networks (CNNs) typically accept a raw EEG signal as input, encompassing both the information associated with the evoked potential and the background activity, which can potentially diminish predictive accuracy. Our approach involves advanced Single-Trial (ST) ErrP enhancement techniques for processing raw EEG signals in the initial stage, followed by CNNs for discerning between ErrP and NonErrP segments in the second stage. We tested different combinations of methods and CNNs. As far as ST ErrP estimation is concerned, we examined various methods encompassing subspace regularization techniques, Continuous Wavelet Transform, and ARX models. For the classification stage, we evaluated the performance of EEGNet, CNN, and a Siamese Neural Network. A comparative analysis against the method of directly applying CNNs to raw EEG signals revealed the advantages of our architecture. Leveraging subspace regularization yielded the best improvement in classification metrics, at up to 14% in balanced accuracy and 13.4% in F1-score.

RevDate: 2023-11-27

Su K, Qiu Z, J Xu (2023)

A 14-Bit, 12 V-to-100 V Voltage Compliance Electrical Stimulator with Redundant Digital Calibration.

Micromachines, 14(11):.

Electrical stimulation is an important technique for modulating the functions of the nervous system through electrical stimulus. To implement a more competitive prototype that can tackle the domain-specific difficulties of existing electrical stimulators, three key techniques are proposed in this work. Firstly, a load-adaptive power saving technique called over-voltage detection is implemented to automatically adjust the supply voltage. Secondly, redundant digital calibration (RDC) is proposed to improve current accuracy and ensure safety during long-term electrical stimulation without costing too much circuit area and power. Thirdly, a flexible waveform generator is designed to provide arbitrary stimulus waveforms for particular applications. Measurement results show the stimulator can adjust the supply voltage from 12 V to 100 V automatically, and the measured effective resolution of the stimulation current reaches 14 bits in a full range of 6.5 mA. Without applying charge balancing techniques, the average mismatch between the cathodic and anodic current pulses in biphasic stimulus is 0.0427%. The proposed electrical stimulator can generate arbitrary stimulus waveforms, including sine, triangle, rectangle, etc., and it is supposed to be competitive for implantable and wearable devices.

RevDate: 2023-11-28

Zhang Y, Zeng H, Zhou H, et al (2023)

Predicting the Outcome of Patients with Aneurysmal Subarachnoid Hemorrhage: A Machine-Learning-Guided Scorecard.

Journal of clinical medicine, 12(22):.

Aneurysmal subarachnoid hemorrhage (aSAH) frequently causes long-term disability, but predicting outcomes remains challenging. Routine parameters such as demographics, admission status, CT findings, and blood tests can be used to predict aSAH outcomes. The aim of this study was to compare the performance of traditional logistic regression with several machine learning algorithms using readily available indicators and to generate a practical prognostic scorecard based on machine learning. Eighteen routinely available indicators were collected as outcome predictors for individuals with aSAH. Logistic regression (LR), random forest (RF), support vector machines (SVMs), and fully connected neural networks (FCNNs) were compared. A scorecard system was established based on predictor weights. The results show that machine learning models and a scorecard achieved 0.75~0.8 area under the curve (AUC) predicting aSAH outcomes (LR 0.739, RF 0.749, SVM 0.762~0.793, scorecard 0.794). FCNNs performed best (~0.95) but lacked interpretability. The scorecard model used only five factors, generating a clinically useful tool with a total cutoff score of ≥5, indicating poor prognosis. We developed and validated machine learning models proven to predict outcomes more accurately in individuals with aSAH. The parameters found to be the most strongly predictive of outcomes were NLR, lymphocyte count, monocyte count, hypertension status, and SEBES. The scorecard system provides a simplified means of applying predictive analytics at the bedside using a few key indicators.

RevDate: 2023-11-27

Popa LL, Chira D, Strilciuc Ș, et al (2023)

Non-Invasive Systems Application in Traumatic Brain Injury Rehabilitation.

Brain sciences, 13(11):.

Traumatic brain injury (TBI) is a significant public health concern, often leading to long-lasting impairments in cognitive, motor and sensory functions. The rapid development of non-invasive systems has revolutionized the field of TBI rehabilitation by offering modern and effective interventions. This narrative review explores the application of non-invasive technologies, including electroencephalography (EEG), quantitative electroencephalography (qEEG), brain-computer interface (BCI), eye tracking, near-infrared spectroscopy (NIRS), functional near-infrared spectroscopy (fNIRS), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) in assessing TBI consequences, and repetitive transcranial magnetic stimulation (rTMS), low-level laser therapy (LLLT), neurofeedback, transcranial direct current stimulation (tDCS), transcranial alternative current stimulation (tACS) and virtual reality (VR) as therapeutic approaches for TBI rehabilitation. In pursuit of advancing TBI rehabilitation, this narrative review highlights the promising potential of non-invasive technologies. We emphasize the need for future research and clinical trials to elucidate their mechanisms of action, refine treatment protocols, and ensure their widespread adoption in TBI rehabilitation settings.

RevDate: 2023-11-27

Lian J, Qiao X, Zhao Y, et al (2023)

EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions.

Brain sciences, 13(11):.

Although target detection based on electroencephalogram (EEG) signals has been extensively investigated recently, EEG-based target detection under weak hidden conditions remains a problem. In this paper, we proposed a rapid serial visual presentation (RSVP) paradigm for target detection corresponding to five levels of weak hidden conditions quantitively based on the RGB color space. Eighteen subjects participated in the experiment, and the neural signatures, including P300 amplitude and latency, were investigated. Detection performance was evaluated under five levels of weak hidden conditions using the linear discrimination analysis and support vector machine classifiers on different channel sets. The experimental results showed that, compared with the benchmark condition, (1) the P300 amplitude significantly decreased (8.92 ± 1.24 μV versus 7.84 ± 1.40 μV, p = 0.021) and latency was significantly prolonged (582.39 ± 25.02 ms versus 643.83 ± 26.16 ms, p = 0.028) only under the weakest hidden condition, and (2) the detection accuracy decreased by less than 2% (75.04 ± 3.24% versus 73.35 ± 3.15%, p = 0.029) with a more than 90% reduction in channel number (62 channels versus 6 channels), determined using the proposed channel selection method under the weakest hidden condition. Our study can provide new insights into target detection under weak hidden conditions based on EEG signals with a rapid serial visual presentation paradigm. In addition, it may expand the application of brain-computer interfaces in EEG-based target detection areas.

RevDate: 2023-11-24

Xu F, Pan D, Zheng H, et al (2023)

EESCN: A novel spiking neural network method for EEG-based emotion recognition.

Computer methods and programs in biomedicine, 243:107927 pii:S0169-2607(23)00593-X [Epub ahead of print].

BACKGROUND AND OBJECTIVE: Although existing artificial neural networks have achieved good results in electroencephalograph (EEG) emotion recognition, further improvements are needed in terms of bio-interpretability and robustness. In this research, we aim to develop a highly efficient and high-performance method for emotion recognition based on EEG.

METHODS: We propose an Emo-EEGSpikeConvNet (EESCN), a novel emotion recognition method based on spiking neural network (SNN). It consists of a neuromorphic data generation module and a NeuroSpiking framework. The neuromorphic data generation module converts EEG data into 2D frame format as input to the NeuroSpiking framework, while the NeuroSpiking framework is used to extract spatio-temporal features of EEG for classification.

RESULTS: EESCN achieves high emotion recognition accuracies on DEAP and SEED-IV datasets, ranging from 94.56% to 94.81% on DEAP and a mean accuracy of 79.65% on SEED-IV. Compared to existing SNN methods, EESCN significantly improves EEG emotion recognition performance. In addition, it also has the advantages of faster running speed and less memory footprint.

CONCLUSIONS: EESCN has shown excellent performance and efficiency in EEG-based emotion recognition with potential for practical applications requiring portability and resource constraints.

RevDate: 2023-11-23

Ke Y, Wang T, He F, et al (2023)

Enhancing EEG-based cross-day mental workload classification using periodic component of power spectrum.

Journal of neural engineering [Epub ahead of print].

The day-to-day variability of electroencephalogram (EEG) poses a significant challenge to decode human brain activity in EEG-based passive brain-computer interfaces (pBCIs). Conventionally, a time-consuming calibration process is required to collect data from users on a new day to ensure the performance of the machine learning-based decoding model, which hinders the application of pBCIs to monitor mental workload (MWL) states in real-world settings. This study investigated the day-to-day stability of the raw power spectral density (PSD) and their periodic and aperiodic components decomposed by the Fitting Oscillations and One-Over-F algorithm. In addition, we validated the feasibility of using periodic components to improve cross-day MWL classification performance. Compared to the raw PSD (69.9%±18.5%) and the aperiodic component (69.4%±19.2%), the periodic component had better day-to-day stability and significantly higher cross-day classification accuracy (84.2%±11.0%). This finding not only enhances the practicality of pBCIs for MWL estimation but also unlocks the potential for decoding various brain states in future applications.

RevDate: 2023-11-24

Chen S, Zhang X, Shen X, et al (2023)

Online Estimating Pairwise Neuronal Functional Connectivity in 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].

Neurons respond to external stimuli and form functional networks through pairwise interactions. A neural encoding model can describe a single neuron's behavior, and brain-machine interfaces (BMIs) provide a platform to investigate how neurons adapt, functionally connect, and encode movement. Movement modulation and pairwise functional connectivity are modeled as high-dimensional tuning states, estimated from neural spike train observations. However, accurate estimation of this neural state vector can be challenging as pairwise neural interactions are highly dimensional, change in different temporal scales from movement, and could be non-stationary. We propose an Adam-based gradient descent method to online estimate high-dimensional pairwise neuronal functional connectivity and single neuronal tuning adaptation simultaneously. By minimizing negative log-likelihood based on point process observation, the proposed method adaptively adjusts the learning rate for each dimension of the neural state vectors by employing momentum and regularizer. We test the method on real recordings of two rats performing the brain control mode of a two-lever discrimination task. Our results show that our method outperforms existing methods, especially when the state is sparse. Our method is more stable and faster for an online scenario regardless of the parameter initializations. Our method provides a promising tool to track and build the time-variant functional neural connectivity, which dynamically forms the functional network and results in better brain control.

RevDate: 2023-11-24

Zhu L, Liu Y, Liu R, et al (2023)

Decoding Multi-Brain Motor Imagery from EEG Using Coupling Feature Extraction and Few-Shot Learning.

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

Electroencephalography (EEG)-based motor imagery (MI) is one of brain computer interface (BCI) paradigms, which aims to build a direct communication pathway between human brain and external devices by decoding the brain activities. In a traditional way, MI BCI replies on a single brain, which suffers from the limitations, such as low accuracy and weak stability. To alleviate these limitations, multi-brain BCI has emerged based on the integration of multiple individuals' intelligence. Nevertheless, the existing decoding methods mainly use linear averaging or feature integration learning from multi-brain EEG data, and do not effectively utilize coupling relationship features, resulting in undesired decoding accuracy. To overcome these challenges, we proposed an EEG-based multi-brain MI decoding method, which utilizes coupling feature extraction and few-shot learning to capture coupling relationship features among multi-brains with only limited EEG data. We performed an experiment to collect EEG data from multiple persons who engaged in the same task simultaneously and compared the methods on the collected data. The comparison results showed that our proposed method improved the performance by 14.23% compared to the single-brain mode in the 10-shot three-class decoding task. It demonstrated the effectiveness of the proposed method and usability of the method in the context of only small amount of EEG data available.

RevDate: 2023-11-25

Mao C, Gao M, Zang SK, et al (2023)

Orthosteric and allosteric modulation of human HCAR2 signaling complex.

Nature communications, 14(1):7620.

Hydroxycarboxylic acids are crucial metabolic intermediates involved in various physiological and pathological processes, some of which are recognized by specific hydroxycarboxylic acid receptors (HCARs). HCAR2 is one such receptor, activated by endogenous β-hydroxybutyrate (3-HB) and butyrate, and is the target for Niacin. Interest in HCAR2 has been driven by its potential as a therapeutic target in cardiovascular and neuroinflammatory diseases. However, the limited understanding of how ligands bind to this receptor has hindered the development of alternative drugs able to avoid the common flushing side-effects associated with Niacin therapy. Here, we present three high-resolution structures of HCAR2-Gi1 complexes bound to four different ligands, one potent synthetic agonist (MK-6892) bound alone, and the two structures bound to the allosteric agonist compound 9n in conjunction with either the endogenous ligand 3-HB or niacin. These structures coupled with our functional and computational analyses further our understanding of ligand recognition, allosteric modulation, and activation of HCAR2 and pave the way for the development of high-efficiency drugs with reduced side-effects.

RevDate: 2023-11-22

Yu C, Lu Y, Pang J, et al (2023)

A hemostatic sponge derived from chitosan and hydroxypropylmethylcellulose.

Journal of the mechanical behavior of biomedical materials, 150:106240 pii:S1751-6161(23)00593-3 [Epub ahead of print].

Hemostatic materials are of great significance for rapid control of bleeding, especially in military trauma and traffic accidents. Chitosan (CS) hemostatic sponges have been widely concerned and studied due to their excellent biocompatibility. However, the hemostatic performance of pure chitosan sponges is poor due to the shortcoming of strong rigidity. In this study, CS and hydroxypropylmethylcellulose (HPMC) were combined to develop a safe and effective hemostatic composite sponges (CS/HPMC) for hemorrhage control by a simple mixed-lyophilization strategy. The CS/HPMC exhibited excellent flexibility (the flexibility was 74% higher than that of pure CS sponges). Due to the high porosity and procoagulant chemical structure of the CS/HPMC, it exhibited rapid hemostatic ability in vitro (BCI was shortened by 50% than that of pure CS sponges). The good biocompatibility of the obtained CS/HPMC was confirmed via cytotoxicity, hemocompatibility and skin irritation tests. The CS/HPMC can induced the erythrocyte and platelets adhesion, resulting in significant coagulation acceleration. The CS/HPMC had excellent performance in vivo assessments with shortest clotting time (40 s) and minimal blood loss (166 mg). All above results proved that the CS/HPMC had great potential to be a safe and rapid hemostatic material.

RevDate: 2023-11-22

Khan S, Anderson W, T Constandinou (2023)

Surgical Implantation of Brain Computer Interfaces.

JAMA surgery pii:2812289 [Epub ahead of print].

RevDate: 2023-11-22

Mizuguchi N (2023)

Candidate brain regions for motor imagery practice: a commentary on Rieger et al., 2023.

Psychological research [Epub ahead of print].

The mechanism through which motor imagery practice improves motor performance remains unclear. In this special issue, Rieger et al. propose a model to explain why motor imagery practice improves motor performance. According to their model, motor imagery involves a comparison between intended and predicted action effects, allowing for the modification of the internal model upon detecting errors. I believe that the anterior cingulate cortex (ACC) is a candidate as a brain region responsible for comparing intended and predicted action effects. Evidence supports this hypothesis, as a previous study has observed error-related activity in the ACC preceding incorrect responses (i.e., commission errors) in the Go/No-go task (Bediou et al., 2012, Neuroimage). Therefore, the error-related activity can be induced without any feedback. This fact also sheds light on the mechanisms of brain-computer interface. I believe that this additional literature will enhance Rieger's model.

RevDate: 2023-11-23
CmpDate: 2023-11-23

Xu F, Yan Y, Zhu J, et al (2023)

Self-Supervised EEG Representation Learning with Contrastive Predictive Coding for Post-Stroke Patients.

International journal of neural systems, 33(12):2350066.

Stroke patients are prone to fatigue during the EEG acquisition procedure, and experiments have high requirements on cognition and physical limitations of subjects. Therefore, how to learn effective feature representation is very important. Deep learning networks have been widely used in motor imagery (MI) based brain-computer interface (BCI). This paper proposes a contrast predictive coding (CPC) framework based on the modified s-transform (MST) to generate MST-CPC feature representations. MST is used to acquire the temporal-frequency feature to improve the decoding performance for MI task recognition. EEG2Image is used to convert multi-channel one-dimensional EEG into two-dimensional EEG topography. High-level feature representations are generated by CPC which consists of an encoder and autoregressive model. Finally, the effectiveness of generated features is verified by the k-means clustering algorithm. It can be found that our model generates features with high efficiency and a good clustering effect. After classification performance evaluation, the average classification accuracy of MI tasks is 89% based on 40 subjects. The proposed method can obtain effective feature representations and improve the performance of MI-BCI systems. By comparing several self-supervised methods on the public dataset, it can be concluded that the MST-CPC model has the highest average accuracy. This is a breakthrough in the combination of self-supervised learning and image processing of EEG signals. It is helpful to provide effective rehabilitation training for stroke patients to promote motor function recovery.

RevDate: 2023-11-24

Colamarino E, Lorusso M, Pichiorri F, et al (2023)

DiSCIoser: unlocking recovery potential of arm sensorimotor functions after spinal cord injury by promoting activity-dependent brain plasticity by means of brain-computer interface technology: a randomized controlled trial to test efficacy.

BMC neurology, 23(1):414.

BACKGROUND: Traumatic cervical spinal cord injury (SCI) results in reduced sensorimotor abilities that strongly impact on the achievement of daily living activities involving hand/arm function. Among several technology-based rehabilitative approaches, Brain-Computer Interfaces (BCIs) which enable the modulation of electroencephalographic sensorimotor rhythms, are promising tools to promote the recovery of hand function after SCI. The "DiSCIoser" study proposes a BCI-supported motor imagery (MI) training to engage the sensorimotor system and thus facilitate the neuroplasticity to eventually optimize upper limb sensorimotor functional recovery in patients with SCI during the subacute phase, at the peak of brain and spinal plasticity. To this purpose, we have designed a BCI system fully compatible with a clinical setting whose efficacy in improving hand sensorimotor function outcomes in patients with traumatic cervical SCI will be assessed and compared to the hand MI training not supported by BCI.

METHODS: This randomized controlled trial will include 30 participants with traumatic cervical SCI in the subacute phase randomly assigned to 2 intervention groups: the BCI-assisted hand MI training and the hand MI training not supported by BCI. Both interventions are delivered (3 weekly sessions; 12 weeks) as add-on to standard rehabilitation care. A multidimensional assessment will be performed at: randomization/pre-intervention and post-intervention. Primary outcome measure is the Graded Redefined Assessment of Strength, Sensibility and Prehension (GRASSP) somatosensory sub-score. Secondary outcome measures include the motor and functional scores of the GRASSP and other clinical, neuropsychological, neurophysiological and neuroimaging measures.

DISCUSSION: We expect the BCI-based intervention to promote meaningful cortical sensorimotor plasticity and eventually maximize recovery of arm functions in traumatic cervical subacute SCI. This study will generate a body of knowledge that is fundamental to drive optimization of BCI application in SCI as a top-down therapeutic intervention, thus beyond the canonical use of BCI as assistive tool.

TRIAL REGISTRATION: Name of registry: DiSCIoser: improving arm sensorimotor functions after spinal cord injury via brain-computer interface training (DiSCIoser).

TRIAL REGISTRATION NUMBER: NCT05637775; registration date on the platform: 05-12-2022.

RevDate: 2023-11-21

Wu EG, Rudzite AM, Bohlen MO, et al (2023)

Decomposition of retinal ganglion cell electrical images for cell type and functional inference.

bioRxiv : the preprint server for biology pii:2023.11.06.565889.

Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision. The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose electrical image into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments. Large-scale multi-electrode recordings from the macaque retina were used to test the effectiveness of the decomposition. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells), a substantial advance. Together, these findings may contribute to more accurate inference of RGC types and their original light responses in the degenerated retina, with possible implications for other electrical imaging applications.

RevDate: 2023-11-24

Chen K, Forrest A, Gonzalez Burgos G, et al (2023)

Neuronal functional connectivity is impaired in a layer dependent manner near the chronically implanted microelectrodes.

bioRxiv : the preprint server for biology pii:2023.11.06.565852.

OBJECTIVE: This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near the chronically implanted microelectrode. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how does the FBR impact affect the functional stability of neural circuits near implanted Brain-Computer Interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders.

APPROACH: This study utilized multisite Michigan-style microelectrodes that span all cortical layers and the hippocampal CA1 region to collect spontaneous and visually-evoked electrophysiological activity. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of LFP and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation.

MAIN RESULTS: The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations.

SIGNIFICANCE: This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study's understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.

RevDate: 2023-11-21

Fan C, Hahn N, Kamdar F, et al (2023)

Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication.

ArXiv pii:2311.03611.

Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.

RevDate: 2023-11-20

Borgheai SB, Zisk AH, McLinden J, et al (2023)

Multimodal pre-screening can predict BCI performance variability: A novel subject-specific experimental scheme.

Computers in biology and medicine, 168:107658 pii:S0010-4825(23)01123-X [Epub ahead of print].

BACKGROUND: Brain-computer interface (BCI) systems currently lack the required robustness for long-term daily use due to inter- and intra-subject performance variability. In this study, we propose a novel personalized scheme for a multimodal BCI system, primarily using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), to identify, predict, and compensate for factors affecting competence-related and interfering factors associated with performance.

METHOD: 11 (out of 13 recruited) participants, including five participants with motor deficits, completed four sessions on average. During the training sessions, the subjects performed a short pre-screening phase, followed by three variations of a novel visou-mental (VM) protocol. Features extracted from the pre-screening phase were used to construct predictive platforms using stepwise multivariate linear regression (MLR) models. In the test sessions, we employed a task-correction phase where our predictive models were used to predict the ideal task variation to maximize performance, followed by an interference-correction phase. We then investigated the associations between predicted and actual performances and evaluated the outcome of correction strategies.

RESULT: The predictive models resulted in respective adjusted R-squared values of 0.942, 0.724, and 0.939 for the first, second, and third variation of the task, respectively. The statistical analyses showed significant associations between the performances predicted by predictive models and the actual performances for the first two task variations, with rhos of 0.7289 (p-value = 0.011) and 0.6970 (p-value = 0.017), respectively. For 81.82 % of the subjects, the task/workload correction stage correctly determined which task variation provided the highest accuracy, with an average performance gain of 5.18 % when applying the correction strategies.

CONCLUSION: Our proposed method can lead to an integrated multimodal predictive framework to compensate for BCI performance variability, particularly, for people with severe motor deficits.

RevDate: 2023-11-20

Li M, Li J, Song Z, et al (2023)

EEGNet-based multi-source domain filter for BCI transfer learning.

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

Deep learning has great potential on decoding EEG in brain-computer interface. While common deep learning algorithms cannot directly train models with data from multiple individuals because of the inter-individual differences in EEG. Collecting enough data for each subject to satisfy the training of deep learning would result in an increase in training cost. This study proposes a novel transfer learning, EEGNet-based multi-source domain filter for transfer learning (EEGNet-MDFTL), to reduce the amount of training data and improve the performance of BCI. The EEGNet-MDFTL uses bagging ensemble learning to learn domain-invariant features from the multi-source domain and utilizes model loss value to filter the multi-source domain. Compared with baseline methods, the accuracy of the EEGNet-MDFTL reaches 91.96%, higher than two state-of-the-art methods, which demonstrates source domain filter can select similar source domains to improve the accuracy of the model, and remains a high level even when the data amount is reduced to 1/8, proving that ensemble learning learns enough domain invariant features from the multi-source domain to make the model insensitive to data amount. The proposed EEGNet-MDFTL is effective in improving the decoding performance with a small amount of data, which is helpful to save the BCI training cost.

RevDate: 2023-11-20

Herring EZ, Graczyk EL, Memberg WD, et al (2023)

Reconnecting the Hand and Arm to the Brain: Efficacy of Neural Interfaces for Sensorimotor Restoration After Tetraplegia.

Neurosurgery pii:00006123-990000000-00967 [Epub ahead of print].

BACKGROUND AND OBJECTIVES: Paralysis after spinal cord injury involves damage to pathways that connect neurons in the brain to peripheral nerves in the limbs. Re-establishing this communication using neural interfaces has the potential to bridge the gap and restore upper extremity function to people with high tetraplegia. We report a novel approach for restoring upper extremity function using selective peripheral nerve stimulation controlled by intracortical microelectrode recordings from sensorimotor networks, along with restoration of tactile sensation of the hand using intracortical microstimulation.

METHODS: A 27-year-old right-handed man with AIS-B (motor-complete, sensory-incomplete) C3-C4 tetraplegia was enrolled into the clinical trial. Six 64-channel intracortical microelectrode arrays were implanted into left hemisphere regions involved in upper extremity function, including primary motor and sensory cortices, inferior frontal gyrus, and anterior intraparietal area. Nine 16-channel extraneural peripheral nerve electrodes were implanted to allow targeted stimulation of right median, ulnar (2), radial, axillary, musculocutaneous, suprascapular, lateral pectoral, and long thoracic nerves, to produce selective muscle contractions on demand. Proof-of-concept studies were performed to demonstrate feasibility of using a brain-machine interface to read from and write to the brain for restoring motor and sensory functions of the participant's own arm and hand.

RESULTS: Multiunit neural activity that correlated with intended motor action was successfully recorded from intracortical arrays. Microstimulation of electrodes in somatosensory cortex produced repeatable sensory percepts of individual fingers for restoration of touch sensation. Selective electrical activation of peripheral nerves produced antigravity muscle contractions, resulting in functional movements that the participant was able to command under brain control to perform virtual and actual arm and hand movements. The system was well tolerated with no operative complications.

CONCLUSION: The combination of implanted cortical electrodes and nerve cuff electrodes has the potential to create bidirectional restoration of motor and sensory functions of the arm and hand after neurological injury.

RevDate: 2023-11-20

Quanyu W, Sheng D, Weige T, et al (2023)

Research on MI EEG signal classification algorithm using multi-model fusion strategy coupling.

Computer methods in biomechanics and biomedical engineering [Epub ahead of print].

To enhance the accuracy of motor imagery(MI)EEG signal recognition, two methods, namely power spectral density and wavelet packet decomposition combined with a common spatial pattern, were employed to explore the feature information in depth in MI EEG signals. The extracted MI EEG signal features were subjected to series feature fusion, and the F-test method was used to select features with higher information content. Here regarding the accuracy of MI EEG signal classification, we further proposed the Platt Scaling probability calibration method was used to calibrate the results obtained from six basic classifiers, namely random forest (RF), support vector machines (SVM), Logistic Regression (LR), Gaussian naïve bayes (GNB), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). From these 12 classifiers, three to four with higher accuracy were selected for model fusion. The proposed method was validated on Datasets 2a of the 4th International BCI Competition, achieving an average accuracy of MI EEG data of nine subjects reached 91.46%, which indicates that model fusion was an effective method to improve classification accuracy, and provides some reference value for the research on MI brain-machine interface.

RevDate: 2023-11-19

Ali O, Saif-Ur-Rehman M, Glasmachers T, et al (2023)

ConTraNet: A hybrid network for improving the classification of EEG and EMG signals with limited training data.

Computers in biology and medicine, 168:107649 pii:S0010-4825(23)01114-9 [Epub ahead of print].

OBJECTIVE: Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data.

APPROACH: In this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals.

MAIN RESULTS: We evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks).

SIGNIFICANCE: With limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.

RevDate: 2023-11-21
CmpDate: 2023-11-20

Canny E, Vansteensel MJ, van der Salm SMA, et al (2023)

Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome.

Journal of neuroengineering and rehabilitation, 20(1):157.

Individuals with a locked-in state live with severe whole-body paralysis that limits their ability to communicate with family and loved ones. Recent advances in brain-computer interface (BCI) technology have presented a potential alternative for these people to communicate by detecting neural activity associated with attempted hand or speech movements and translating the decoded intended movements to a control signal for a computer. A technique that could potentially enrich the communication capacity of BCIs is functional electrical stimulation (FES) of paralyzed limbs and face to restore body and facial movements of paralyzed individuals, allowing to add body language and facial expression to communication BCI utterances. Here, we review the current state of the art of existing BCI and FES work in people with paralysis of body and face and propose that a combined BCI-FES approach, which has already proved successful in several applications in stroke and spinal cord injury, can provide a novel promising mode of communication for locked-in individuals.

RevDate: 2023-11-21
CmpDate: 2023-11-20

Tanamachi K, Kuwahara W, Okawada M, et al (2023)

Relationship between resting-state functional connectivity and change in motor function after motor imagery intervention in patients with stroke: a scoping review.

Journal of neuroengineering and rehabilitation, 20(1):159.

BACKGROUND: In clinical practice, motor imagery has been proposed as a treatment modality for stroke owing to its feasibility in patients with severe motor impairment. Motor imagery-based interventions can be categorized as open- or closed-loop. Closed-loop intervention is based on voluntary motor imagery and induced peripheral sensory afferent (e.g., Brain Computer Interface (BCI)-based interventions). Meanwhile, open-loop interventions include methods without voluntary motor imagery or sensory afferent. Resting-state functional connectivity (rs-FC) is defined as a significant temporal correlated signal among functionally related brain regions without any stimulus. rs-FC is a powerful tool for exploring the baseline characteristics of brain connectivity. Previous studies reported changes in rs-FC after motor imagery interventions. Systematic reviews also reported the effects of motor imagery-based interventions at the behavioral level. This study aimed to review and describe the relationship between the improvement in motor function and changes in rs-FC after motor imagery in patients with stroke.

REVIEW PROCESS: The literature review was based on Arksey and O'Malley's framework. PubMed, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and Web of Science were searched up to September 30, 2023. The included studies covered the following topics: illusion without voluntary action, motor imagery, action imitation, and BCI-based interventions. The correlation between rs-FC and motor function before and after the intervention was analyzed. After screening by two independent researchers, 13 studies on BCI-based intervention, motor imagery intervention, and kinesthetic illusion induced by visual stimulation therapy were included.

CONCLUSION: All studies relating to motor imagery in this review reported improvement in motor function post-intervention. Furthermore, all those studies demonstrated a significant relationship between the change in motor function and rs-FC (e.g., sensorimotor network and parietal cortex).

RevDate: 2023-11-18

Sengupta P, K Lakshminarayanan (2023)

Cortical Activation and BCI Performance during Brief Vibrotactile Tactile Imagery: A Comparative Study with Motor Imagery.

Behavioural brain research pii:S0166-4328(23)00478-3 [Epub ahead of print].

Brain-computer interfaces (BCIs) rely heavily on motor imagery (MI) for operation, yet tactile imagery (TI) presents a novel approach that may be advantageous in situations where visual feedback is impractical. The current study aimed to compare the cortical activity and digit classification performance induced by TI and MI to assess the viability of TI for use in BCIs. Twelve right-handed participants engaged in trials of TI and MI, focusing on their left and right index digits. Event-related desynchronization (ERD) in the mu and beta bands was analyzed, and classification accuracy was determined through an artificial neural network (ANN). Comparable ERD patterns were observed in both TI and MI, with significant decreases in ERD during imagery tasks. The ANN demonstrated high classification accuracy, with TI achieving a mean±SD of 79.30±3.91% and MI achieving 81.10±2.96%, with no significant difference between the two (p=0.11). The study found that TI induces substantial ERD comparable to MI and maintains high classification accuracy, supporting its potential as an effective mental strategy for BCIs. This suggests that TI could be a valuable alternative in BCI applications, particularly for individuals unable to rely on visual cues.

RevDate: 2023-11-18

Ousingsawat J, Centeio R, Schreiber R, et al (2023)

Niclosamide, but not ivermectin, inhibits anoctamin 1 and 6 and attenuates inflammation of the respiratory tract.

Pflugers Archiv : European journal of physiology [Epub ahead of print].

Inflammatory airway diseases like cystic fibrosis, asthma and COVID-19 are characterized by high levels of pulmonary cytokines. Two well-established antiparasitic drugs, niclosamide and ivermectin, are intensively discussed for the treatment of viral inflammatory airway infections. Here, we examined these repurposed drugs with respect to their anti-inflammatory effects in airways in vivo and in vitro. Niclosamide reduced mucus content, eosinophilic infiltration and cell death in asthmatic mouse lungs in vivo and inhibited release of interleukins in the two differentiated airway epithelial cell lines CFBE and BCi-NS1.1 in vitro. Cytokine release was also inhibited by the knockdown of the Ca[2+]-activated Cl[-] channel anoctamin 1 (ANO1, TMEM16A) and the phospholipid scramblase anoctamin 6 (ANO6, TMEM16F), which have previously been shown to affect intracellular Ca[2+] levels near the plasma membrane and to facilitate exocytosis. At concentrations around 200 nM, niclosamide inhibited inflammation, lowered intracellular Ca[2+], acidified cytosolic pH and blocked activation of ANO1 and ANO6. It is suggested that niclosamide brings about its anti-inflammatory effects at least in part by inhibiting ANO1 and ANO6, and by lowering intracellular Ca[2+] levels. In contrast to niclosamide, 1 µM ivermectin did not exert any of the effects described for niclosamide. The present data suggest niclosamide as an effective anti-inflammatory treatment in CF, asthma, and COVID-19, in addition to its previously reported antiviral effects. It has an advantageous concentration-response relationship and is known to be well tolerated.

RevDate: 2023-11-20

Wang DX, Dong ZJ, Deng SX, et al (2023)

GDF11 slows excitatory neuronal senescence and brain ageing by repressing p21.

Nature communications, 14(1):7476.

As a major neuron type in the brain, the excitatory neuron (EN) regulates the lifespan in C. elegans. How the EN acquires senescence, however, is unknown. Here, we show that growth differentiation factor 11 (GDF11) is predominantly expressed in the EN in the adult mouse, marmoset and human brain. In mice, selective knock-out of GDF11 in the post-mitotic EN shapes the brain ageing-related transcriptional profile, induces EN senescence and hyperexcitability, prunes their dendrites, impedes their synaptic input, impairs object recognition memory and shortens the lifespan, establishing a functional link between GDF11, brain ageing and cognition. In vitro GDF11 deletion causes cellular senescence in Neuro-2a cells. Mechanistically, GDF11 deletion induces neuronal senescence via Smad2-induced transcription of the pro-senescence factor p21. This work indicates that endogenous GDF11 acts as a brake on EN senescence and brain ageing.

RevDate: 2023-11-20
CmpDate: 2023-11-20

Iwane F, Billard A, JDR Millán (2023)

Inferring individual evaluation criteria for reaching trajectories with obstacle avoidance from EEG signals.

Scientific reports, 13(1):20163.

During reaching actions, the human central nerve system (CNS) generates the trajectories that optimize effort and time. When there is an obstacle in the path, we make sure that our arm passes the obstacle with a sufficient margin. This comfort margin varies between individuals. When passing a fragile object, risk-averse individuals may adopt a larger margin by following the longer path than risk-prone people do. However, it is not known whether this variation is associated with a personalized cost function used for the individual optimal control policies and how it is represented in our brain activity. This study investigates whether such individual variations in evaluation criteria during reaching results from differentiated weighting given to energy minimization versus comfort, and monitors brain error-related potentials (ErrPs) evoked when subjects observe a robot moving dangerously close to a fragile object. Seventeen healthy participants monitored a robot performing safe, daring and unsafe trajectories around a wine glass. Each participant displayed distinct evaluation criteria on the energy efficiency and comfort of robot trajectories. The ErrP-BCI outputs successfully inferred such individual variation. This study suggests that ErrPs could be used in conjunction with an optimal control approach to identify the personalized cost used by CNS. It further opens new avenues for the use of brain-evoked potential to train assistive robotic devices through the use of neuroprosthetic interfaces.

RevDate: 2023-11-19

Karikari E, KA Koshechkin (2023)

Review on brain-computer interface technologies in healthcare.

Biophysical reviews, 15(5):1351-1358.

Brain-computer interface (BCI) technologies have developed as a game changer, altering how humans interact with computers and opening up new avenues for understanding and utilizing the power of the human brain. The goal of this research study is to assess recent breakthroughs in BCI technologies and their future prospects. The paper starts with an outline of the fundamental concepts and principles that underpin BCI technologies. It examines the many forms of BCIs, including as invasive, partially invasive, and non-invasive interfaces, emphasizing their advantages and disadvantages. The progress of BCI hardware and signal processing techniques is investigated, with a focus on the shift from bulky and invasive systems to more portable and user-friendly options. Following that, the article delves into the important advances in BCI applications across several fields. It investigates the use of BCIs in healthcare, particularly in neurorehabilitation, assistive technology, and cognitive enhancement. BCIs' potential for boosting human capacities such as communication, motor control, and sensory perception is being thoroughly researched. Furthermore, the article investigates developing BCI applications in gaming, entertainment, and virtual reality, demonstrating how BCI technologies are growing outside medical and therapeutic settings. The study also gives light on the problems and limits that prevent BCIs from being widely adopted. Ethical concerns about privacy, data security, and informed permission are addressed, highlighting the importance of strong legislative frameworks to enable responsible and ethical usage of BCI technologies. Furthermore, the study delves into technological issues such as increasing signal resolution and precision, increasing system reliability, and enabling smooth connection with existing technology. Finally, this study paper gives an in-depth examination of the advances and future possibilities of BCI technologies. It emphasizes the transformative influence of BCIs on human-computer interaction and their potential to alter healthcare, gaming, and other industries. This research intends to stimulate further innovation and progress in the field of brain-computer interfaces by addressing problems and imagining future possibilities.

RevDate: 2023-11-18

Choi YJ, Kwon OS, SP Kim (2023)

Design of auditory P300-based brain-computer interfaces with a single auditory channel and no visual support.

Cognitive neurodynamics, 17(6):1401-1416.

UNLABELLED: Non-invasive brain-computer interfaces (BCIs) based on an event-related potential (ERP) component, P300, elicited via the oddball paradigm, have been extensively developed to enable device control and communication. While most P300-based BCIs employ visual stimuli in the oddball paradigm, auditory P300-based BCIs also need to be developed for users with unreliable gaze control or limited visual processing. Specifically, auditory BCIs without additional visual support or multi-channel sound sources can broaden the application areas of BCIs. This study aimed to design optimal stimuli for auditory BCIs among artificial (e.g., beep) and natural (e.g., human voice and animal sounds) sounds in such circumstances. In addition, it aimed to investigate differences between auditory and visual stimulations for online P300-based BCIs. As a result, natural sounds led to both higher online BCI performance and larger differences in ERP amplitudes between the target and non-target compared to artificial sounds. However, no single type of sound offered the best performance for all subjects; rather, each subject indicated different preferences between the human voice and animal sound. In line with previous reports, visual stimuli yielded higher BCI performance (average 77.56%) than auditory counterparts (average 54.67%). In addition, spatiotemporal patterns of the differences in ERP amplitudes between target and non-target were more dynamic with visual stimuli than with auditory stimuli. The results suggest that selecting a natural auditory stimulus optimal for individual users as well as making differences in ERP amplitudes between target and non-target stimuli more dynamic may further improve auditory P300-based BCIs.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-022-09901-3.

RevDate: 2023-11-20
CmpDate: 2023-11-20

Cipriani M, Pichiorri F, Colamarino E, et al (2023)

The Promotoer, a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke: a statistical analysis plan for a randomized controlled trial.

Trials, 24(1):736.

BACKGROUND: Electroencephalography (EEG)-based brain-computer interfaces (BCIs) allow to modulate the sensorimotor rhythms and are emerging technologies for promoting post-stroke motor function recovery. The Promotoer study aims to assess the short and long-term efficacy of the Promotoer system, an EEG-based BCI assisting motor imagery (MI) practice, in enhancing post-stroke functional hand motor recovery. This paper details the statistical analysis plan of the Promotoer study.

METHODS: The Promotoer study is a randomized, controlled, assessor-blinded, single-centre, superiority trial, with two parallel groups and a 1:1 allocation ratio. Subacute stroke patients are randomized to EEG-based BCI-assisted MI training or to MI training alone (i.e. no BCI). An internal pilot study for sample size re-assessment is planned. The primary outcome is the effectiveness of the Upper Extremity Fugl-Meyer Assessment (UE-FMA) score. Secondary outcomes include clinical, functional, and user experience scores assessed at the end of intervention and at follow-up. Neurophysiological assessments are also planned. Effectiveness formulas have been specified, and intention-to-treat and per-protocol populations have been defined. Statistical methods for comparisons of groups and for development of a predictive score of significant improvement are described. Explorative subgroup analyses and methodology to handle missing data are considered.

DISCUSSION: The Promotoer study will provide robust evidence for the short/long-term efficacy of the Promotoer system in subacute stroke patients undergoing a rehabilitation program. Moreover, the development of a predictive score of response will allow transferring of the Promotoer system to optimal clinical practice. By carefully describing the statistical principles and procedures, the statistical analysis plan provides transparency in the analysis of data.

TRIAL REGISTRATION: NCT04353297 . Registered on April 15, 2020.

RevDate: 2023-11-16

Wu Y, Li BZ, Wang L, et al (2023)

An unsupervised real-time spike sorting system based on optimized OSort.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: The OSort algorithm, a pivotal unsupervised spike sorting method, has been implemented in dedicated hardware devices for real-time spike sorting. However, due to the inherent complexity of neural recording environments, OSort still grapples with numerous transient cluster occurrences during the practical sorting process. This leads to substantial memory usage, heavy computational load, and complex hardware architectures, especially in noisy recordings and multi-channel systems.

APPROACH: This study introduces an optimized OSort algorithm (opt-OSort) which utilizes correlation coefficient (CC), instead of Euclidean distance as classification criterion. The CC method not only bolsters the robustness of spike classification amidst the diverse and ever-changing conditions of physiological and recording noise environments, but also can finish the entire sorting procedure within a fixed number of cluster slots, thus preventing a large number of transient clusters. Moreover, the opt-OSort incorporates two configurable validation loops to efficiently reject cluster outliers and track recording variations caused by electrode drifting in real-time.

MAIN RESULTS: The opt-OSort significantly reduces transient cluster occurrences by two orders of magnitude and decreases memory usage by 2.5 to 80 times in the number of pre-allocated transient clusters compared with other hardware implementations of OSort. The opt-OSort maintains an accuracy comparable to offline OSort and other commonly-used algorithms, with a sorting time of 0.68 µs as measured by the hardware-implemented system in both simulated datasets and experimental data. The opt-OSort's ability to handle variations in neural activity caused by electrode drifting is also demonstrated.

SIGNIFICANCE: These results present a rapid, precise, and robust spike sorting solution suitable for integration into low-power, portable, closed-loop neural control systems and brain-computer interfaces.

RevDate: 2023-11-16

Jiang X, Meng L, Wang Z, et al (2023)

Deep Source Semi-Supervised Transfer Learning (DS3TL) for Cross-Subject EEG Classification.

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

OBJECTIVE: An electroencephalogram (EEG) based brain-computer interface (BCI) maps the user's EEG signals into commands for external device control. Usually a large amount of labeled EEG trials are required to train a reliable EEG recognition model. However, acquiring labeled EEG data is time-consuming and user-unfriendly. Semi-supervised learning (SSL) and transfer learning can be used to exploit the unlabeled data and the auxiliary data, respectively, to reduce the amount of labeled data for a new subject.

METHODS: This paper proposes deep source semi-supervised transfer learning (DS3TL) for EEG-based BCIs, which assumes the source subject has a small number of labeled EEG trials and a large number of unlabeled ones, whereas all EEG trials from the target subject are unlabeled. DS3TL mainly includes a hybrid SSL module, a weakly-supervised contrastive module, and a domain adaptation module. The hybrid SSL module integrates pseudo-labeling and consistency regularization for SSL. The weakly-supervised contrastive module performs contrastive learning by using the true labels of the labeled data and the pseudo-labels of the unlabeled data. The domain adaptation module reduces the individual differences by uncertainty reduction.

RESULTS: Experiments on three EEG datasets from different tasks demonstrated that DS3TL outperformed a supervised learning baseline with many more labeled training data, and multiple state-of-the-art SSL approaches with the same number of labeled data.

SIGNIFICANCE: To our knowledge, this is the first approach in EEG-based BCIs that exploits the unlabeled source data for more accurate target classifier training.

RevDate: 2023-11-16

Biffl WL, Fawley JA, RC Mohan (2023)

Diagnosis and Management of Blunt Cardiac Injury: What You Need to Know.

The journal of trauma and acute care surgery pii:01586154-990000000-00569 [Epub ahead of print].

Blunt cardiac injury (BCI) encompasses a wide spectrum, from occult and inconsequential contusion to rapidly fatal cardiac rupture. A small percentage of patients present with abnormal electrocardiogram (ECG) or shock, but most are initially asymptomatic. The potential for sudden dysrhythmia or cardiac pump failure mandates consideration of the presence of BCI, including appropriate monitoring and management. In this review we will present what you need to know to diagnose and manage BCI.

RevDate: 2023-11-15

Xu F, Ming D, Jung TP, et al (2023)

Editorial: The application of artificial intelligence in brain-computer interface and neural system rehabilitation.

Frontiers in neuroscience, 17:1290961.

RevDate: 2023-11-12
CmpDate: 2023-11-09

Agarwal AK, Roy-Chaudhury P, Mounts P, et al (2023)

Taurolidine/Heparin Lock Solution and Catheter-Related Bloodstream Infection in Hemodialysis: A Randomized, Double-Blind, Active-Control, Phase 3 Study.

Clinical journal of the American Society of Nephrology : CJASN, 18(11):1446-1455.

BACKGROUND: Catheter-related bloodstream infections (CRBSIs) are one of the most prevalent, fatal, and costly complications of hemodialysis with a central venous catheter (CVC). The LOCK IT-100 trial compared the efficacy and safety of a taurolidine/heparin catheter lock solution that combines taurolidine 13.5 mg/ml and heparin (1000 units/ml) versus heparin in preventing CRBSIs in participants receiving hemodialysis via CVC.

METHODS: LOCK IT-100 was a randomized, double-blind, active-control, multicenter, phase 3 study that enrolled adults with kidney failure undergoing maintenance hemodialysis via CVC from 70 US sites. Participants were randomized 1:1 to taurolidine/heparin catheter lock solution or heparin control catheter lock solution (1000 units/ml). The primary end point was time to CRBSI as assessed by a blinded Clinical Adjudication Committee. Secondary end points were catheter removal for any reason and loss of catheter patency. On the basis of a prespecified interim analysis, the Data and Safety Monitoring Board recommended terminating the trial early for efficacy with no safety concerns.

RESULTS: In the full analysis population (N =795), nine participants in the taurolidine/heparin arm (n =397; 2%) and 32 participants in the heparin arm (n =398; 8%) had a CRBSI. Event rates per 1000 catheter days were 0.13 and 0.46, respectively, with the difference in time to CRBSI being statistically significant, favoring taurolidine/heparin (P < 0.001). The hazard ratio was 0.29 (95% confidence interval, 0.14 to 0.62), corresponding to a 71% reduction in risk of CRBSIs with taurolidine/heparin versus heparin. There were no significant differences between study arms in time to catheter removal for any reason or loss of catheter patency. The safety of taurolidine/heparin was comparable with that of heparin, and most treatment-emergent adverse events were mild or moderate.

CONCLUSIONS: Taurolidine/heparin reduced the risk of developing a CRBSI in study participants receiving hemodialysis via CVC compared with heparin with a comparable safety profile.

Study Assessing Safety & Effectiveness of a Catheter Lock Solution in Dialysis Patients to Prevent Bloodstream Infection, NCT02651428 .

RevDate: 2023-11-15

Okatan M, M Kocatürk (2023)

Decoding the Spike-Band Subthreshold Motor Cortical Activity.

Journal of motor behavior [Epub ahead of print].

Intracortical Brain-Computer Interfaces (iBCI) use single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP) to control neuroprosthetic devices. SUA and MUA are usually extracted from the bandpassed recording through amplitude thresholding, while subthreshold data are ignored. Here, we show that subthreshold data can actually be decoded to determine behavioral variables with test set accuracy of up to 100%. Although the utility of SUA, MUA and LFP for decoding behavioral variables has been explored previously, this study investigates the utility of spike-band subthreshold activity exclusively. We provide evidence suggesting that this activity can be used to keep decoding performance at acceptable levels even when SUA quality is reduced over time. To the best of our knowledge, the signals that we derive from the subthreshold activity may be the weakest neural signals that have ever been extracted from extracellular neural recordings, while still being decodable with test set accuracy of up to 100%. These results are relevant for the development of fully data-driven and automated methods for amplitude thresholding spike-band extracellular neural recordings in iBCIs containing thousands of electrodes.

RevDate: 2023-11-14

Miao M, Yang Z, Zeng H, et al (2023)

Explainable cross-task adaptive transfer learning for motor imagery EEG classification.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: In the field of motor imagery (MI) electroencephalography (EEG) based brain-computer interfaces (BCIs), deep transfer learning (TL) has proven to be an effective tool for solving the problem of limited availability of subject-specific data for training of robust deep learning (DL) models. Despite considerable progress has been made in cross-subject/session and cross-device scenarios, the more challenging problem of cross-task deep TL still remains largely unexplored.

APPROACH: We propose a novel explainable cross-task adaptive TL method for MI EEG decoding. Firstly, similarity analysis and data alignment are performed for EEG data of motor execution (ME) and MI tasks. Afterward, the MI EEG decoding model is obtained via pre-training with extensive ME EEG data and fine-tuning with partial MI EEG data. Finally, expected gradients based post-hoc explainability analysis is conducted for visualization of important temporal-spatial features.

MAIN RESULTS: Extensive experiments are conducted on one large ME EEG dataset (HGD) and two large MI EEG datasets (openBMI and GIST). The best average classification accuracy of our method reaches 80.00% and 72.73% for openBMI and GIST respectively, which outperforms several state-of-the-art algorithms. Besides, the results of explainability analysis further validate the correlation between ME and MI EEG data and effectiveness of ME/MI cross-task adaptation.

SIGNIFICANCE: This paper confirms that decoding of MI EEG can be well facilitated by pre-existing ME EEG data, which largely relaxes the constraint of training samples for MI EEG decoding and has important practical sense.

RevDate: 2023-11-17
CmpDate: 2023-11-15

Liu M, Jiang N, Shi Y, et al (2023)

Spatiotemporal coding of natural odors in the olfactory bulb.

Journal of Zhejiang University. Science. B, 24(11):1057-1061.


RevDate: 2023-11-18

Francioni V, Tang VD, Brown NJ, et al (2023)

Vectorized instructive signals in cortical dendrites during a brain-computer interface task.

bioRxiv : the preprint server for biology.

Backpropagation of error is the most widely used learning algorithm in artificial neural networks, forming the backbone of modern machine learning and artificial intelligence[1,2]. Backpropagation provides a solution to the credit assignment problem by vectorizing an error signal tailored to individual neurons. Recent theoretical models have suggested that neural circuits could implement backpropagation-like learning by semi-independently processing feedforward and feedback information streams in separate dendritic compartments[3-7]. This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. We designed a neurofeedback brain-computer interface (BCI) task with an experimenter-defined reward function to evaluate the key requirements for dendrites to implement backpropagation-like learning. We trained mice to modulate the activity of two spatially intermingled populations (4 or 5 neurons each) of layer 5 pyramidal neurons in the retrosplenial cortex to rotate a visual grating towards a target orientation while we recorded GCaMP activity from somas and corresponding distal apical dendrites. We observed that the relative magnitudes of somatic versus dendritic signals could be predicted using the activity of the surrounding network and contained information about task-related variables that could serve as instructive signals, including reward and error. The signs of these putative teaching signals both depended on the causal role of individual neurons in the task and predicted changes in overall activity over the course of learning. These results provide the first biological evidence of a backpropagation-like solution to the credit assignment problem in the brain.

RevDate: 2023-11-17
CmpDate: 2023-11-15

Lyu S, RCC Cheung (2023)

Efficient Multiple Channels EEG Signal Classification Based on Hierarchical Extreme Learning Machine.

Sensors (Basel, Switzerland), 23(21):.

The human brain can be seen as one of the most powerful processors in the world, and it has a very complex structure with different kinds of signals for monitoring organics, communicating to neurons, and reacting to different information, which allows large developments in observing human sleeping, revealing diseases, reflecting certain motivations of limbs, and other applications. Relative theory, algorithms, and applications also help us to build brain-computer interface (BCI) systems for different powerful functions. Therefore, we present a fast-reaction framework based on an extreme learning machine (ELM) with multiple layers for the ElectroEncephaloGram (EEG) signals classification in motor imagery, showing the advantages in both accuracy of classification and training speed compared with conventional machine learning methods. The experiments are performed on software with the dataset of BCI Competition II with fast training time and high accuracy. The final average results show an accuracy of 93.90% as well as a reduction of 75% of the training time as compared to conventional deep learning and machine learning algorithms for EEG signal classification, also showing its prospects of the improvement of the performance of the BCI system.

RevDate: 2023-11-17
CmpDate: 2023-11-15

Lun X, Zhang Y, Zhu M, et al (2023)

A Combined Virtual Electrode-Based ESA and CNN Method for MI-EEG Signal Feature Extraction and Classification.

Sensors (Basel, Switzerland), 23(21):.

A Brain-Computer Interface (BCI) is a medium for communication between the human brain and computers, which does not rely on other human neural tissues, but only decodes Electroencephalography (EEG) signals and converts them into commands to control external devices. Motor Imagery (MI) is an important BCI paradigm that generates a spontaneous EEG signal without external stimulation by imagining limb movements to strengthen the brain's compensatory function, and it has a promising future in the field of computer-aided diagnosis and rehabilitation technology for brain diseases. However, there are a series of technical difficulties in the research of motor imagery-based brain-computer interface (MI-BCI) systems, such as: large individual differences in subjects and poor performance of the cross-subject classification model; a low signal-to-noise ratio of EEG signals and poor classification accuracy; and the poor online performance of the MI-BCI system. To address the above problems, this paper proposed a combined virtual electrode-based EEG Source Analysis (ESA) and Convolutional Neural Network (CNN) method for MI-EEG signal feature extraction and classification. The outcomes reveal that the online MI-BCI system developed based on this method can improve the decoding ability of multi-task MI-EEG after training, it can learn generalized features from multiple subjects in cross-subject experiments and has some adaptability to the individual differences of new subjects, and it can decode the EEG intent online and realize the brain control function of the intelligent cart, which provides a new idea for the research of an online MI-BCI system.

RevDate: 2023-11-17

Gunduz ME, Bucak B, Z Keser (2023)

Advances in Stroke Neurorehabilitation.

Journal of clinical medicine, 12(21):.

Stroke is one of the leading causes of disability worldwide despite recent advances in hyperacute interventions to lessen the initial impact of stroke. Stroke recovery therapies are crucial in reducing the long-term disability burden after stroke. Stroke recovery treatment options have rapidly expanded within the last decade, and we are in the dawn of an exciting era of multimodal therapeutic approaches to improve post-stroke recovery. In this narrative review, we highlighted various promising advances in treatment and technologies targeting stroke rehabilitation, including activity-based therapies, non-invasive and minimally invasive brain stimulation techniques, robotics-assisted therapies, brain-computer interfaces, pharmacological treatments, and cognitive therapies. These new therapies are targeted to enhance neural plasticity as well as provide an adequate dose of rehabilitation and improve adherence and participation. Novel activity-based therapies and telerehabilitation are promising tools to improve accessibility and provide adequate dosing. Multidisciplinary treatment models are crucial for post-stroke neurorehabilitation, and further adjuvant treatments with brain stimulation techniques and pharmacological agents should be considered to maximize the recovery. Among many challenges in the field, the heterogeneity of patients included in the study and the mixed methodologies and results across small-scale studies are the cardinal ones. Biomarker-driven individualized approaches will move the field forward, and so will large-scale clinical trials with a well-targeted patient population.

RevDate: 2023-11-13

Qin Y, Li B, Wang W, et al (2023)

ETCNet: An EEG-based motor imagery classification model combining efficient channel attention and temporal convolutional network.

Brain research pii:S0006-8993(23)00444-4 [Epub ahead of print].

Brain-computer interface (BCI) enables the control of external devices using signals from the brain, offering immense potential in assisting individuals with neuromuscular disabilities. Among the different paradigms of BCI systems, the motor imagery (MI) based electroencephalogram (EEG) signal is widely recognized as exceptionally promising. Deep learning (DL) has found extensive applications in the processing of MI signals, wherein convolutional neural networks (CNNs) have demonstrated superior performance compared to conventional machine learning approaches. Nevertheless, challenges related to subject independence and subject dependence persist, while the inherent low signal-to-noise ratio of EEG signals remains a critical aspect that demands attention. Accurately deciphering intentions from EEG signals continues to present a formidable challenge. This paper introduces an advanced end-to-end network that effectively combines the efficient channel attention (ECA) and temporal convolutional network (TCN) components for the classification of motor imagination signals. We incorporated an ECA module prior to feature extraction in order to enhance the extraction of channel-specific features. A compact convolutional network model uses for feature extraction in the middle part. Finally, the time characteristic information is obtained by using TCN. The results show that our network is a lightweight network that is characterized by few parameters and fast speed. Our network achieves an average accuracy of 80.71% on the BCI Competition IV-2a dataset.

RevDate: 2023-11-17

Wang Y, Zhang Y, Zhang Y, et al (2023)

Voluntary Respiration Control: Signature Analysis by EEG.

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

OBJECTIVE: The perception of voluntary respiratory consciousness is quite important in some situations, such as respiratory assistance and respiratory rehabilitation training, and the key signatures about voluntary respiration control may lie in the neural signals from brain manifested as electroencephalography (EEG). The present work aims to explore whether there exists correlation between voluntary respiration and scalp EEG.

METHODS: Evoke voluntary respiration of different intensities, while collecting EEG and respiration signal synchronously. Data from 11 participants were analyzed. Spectrum characteristics at low-frequency band were studied. Computation of EEG-respiration phase lock value (PLV) and EEG sample entropy were conducted as well.

RESULT: When breathing voluntarily, the 0-2 Hz band EEG power is significantly enhanced in frontal and right-parietal area. The distance between main peaks belonging to the two signals in 0-2 Hz spectrum graph tends to get smaller, while EEG-respiration PLV increases in frontal area. Besides, the sample entropy of EEG shows a trend of decreasing during voluntary respiration in both areas.

CONCLUSION: There's a strong correlation between voluntary respiration and scalp EEG.

SIGNIFICANCE: The discoveries will provide guidelines for developing a voluntary respiratory consciousness identifying method and make it possible to monitor people's intention of respiration by noninvasive BCI.

RevDate: 2023-11-10

Cabrera Castillos K, Ladouce S, Darmet L, et al (2023)

Burst c-VEP Based BCI: Optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience.

NeuroImage pii:S1053-8119(23)00597-9 [Epub ahead of print].

The utilization of aperiodic flickering visual stimuli under the form of code-modulated Visual Evoked Potentials (c-VEP) represents a pivotal advancement in the field of reactive Brain-Computer Interface (rBCI). A major advantage of the c-VEP approach is that the training of the model is independent of the number and complexity of targets, which helps reduce calibration time. Nevertheless, the existing designs of c-VEP stimuli can be further improved in terms of visual user experience but also to achieve a higher signal-to-noise ratio, while shortening the selection time and calibration process. In this study, we introduce an innovative variant of code-VEP, referred to as "Burst c-VEP". This original approach involves the presentation of short bursts of aperiodic visual flashes at a deliberately slow rate, typically ranging from two to four flashes per second. The rationale behind this design is to leverage the sensitivity of the primary visual cortex to transient changes in low-level stimuli features to reliably elicit distinctive series of visual evoked potentials. In comparison to other types of faster-paced code sequences, burst c-VEP exhibit favorable properties to achieve high bitwise decoding performance using convolutional neural networks (CNN), which yields potential to attain faster selection time with the need for less calibration data. Furthermore, our investigation focuses on reducing the perceptual saliency of c-VEP through the attenuation of visual stimuli contrast and intensity to significantly improve users' visual comfort. The proposed solutions were tested through an offline 4-classes c-VEP protocol involving 12 participants. Following a factorial design, participants were instructed to focus on c-VEP targets whose pattern (burst and maximum-length sequences) and amplitude (100% or 40% amplitude depth modulations) were manipulated across experimental conditions. Firstly, the full amplitude burst c-VEP sequences exhibited higher accuracy, ranging from 90.5% (with 17.6s of calibration data) to 95.6% (with 52.8s of calibration data), compared to its m-sequence counterpart (71.4% to 85.0%). The mean selection time for both types of codes (1.5s) compared favorably to reports from previous studies. Secondly, our findings revealed that lowering the intensity of the stimuli only slightly decreased the accuracy of the burst code sequences to 94.2% while leading to substantial improvements in terms of user experience. Taken together, these results demonstrate the high potential of the proposed burst codes to advance reactive BCI both in terms of performance and usability. The collected dataset, along with the proposed CNN architecture implementation, are shared through open-access repositories.

RevDate: 2023-11-10

Luo R, Xiao X, Chen E, et al (2023)

Almost free of calibration for SSVEP-based brain-computer interfaces.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is a promising technology that can achieve high information transfer rate (ITR) with supervised algorithms such as ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA). However, training individual models requires a tedious and time-consuming calibration process, which hinders the real-life use of SSVEP-BCIs. A recent data augmentation method, called source aliasing matrix estimation (SAME), can generate new EEG samples from a few calibration trials. But SAME does not exploit the information across stimuli as well as only reduces the number of calibration trials per command, so it still has some limitations.

APPROACH: This study proposes an extended version of SAME, called multi-stimulus SAME (msSAME), which exploits the similarity of the aliasing matrix across frequencies to enhance the performance of SSVEP-BCI with insufficient calibration trials. We also propose a semi-supervised approach based on msSAME that can further reduce the number of SSVEP frequencies needed for calibration. We evaluate our method on two public datasets, Benchmark and BETA, and an online experiment.

MAIN RESULTS: The results show that msSAME outperforms SAME for both eTRCA and TDCA on the public datasets. Moreover, the semi-supervised msSAME-based method achieves comparable performance to the fully calibrated methods and outperforms the conventional free-calibrated methods. Remarkably, our method only needs 24 seconds to calibrate 40 targets in the online experiment and achieves an average ITR of 213.8 bits/min with a peak of 242.6 bits/min.

SIGNIFICANCE: This study significantly reduces the calibration effort for individual SSVEP-BCIs, which is beneficial for developing practical plug-and-play SSVEP-BCIs.

RevDate: 2023-11-10

Das A, Nandi N, S Ray (2023)

Alpha and SSVEP power outperform gamma power in capturing attentional modulation in human EEG.

Cerebral cortex (New York, N.Y. : 1991) pii:7381650 [Epub ahead of print].

Attention typically reduces power in the alpha (8-12 Hz) band and increases power in gamma (>30 Hz) band in brain signals, as reported in macaque local field potential (LFP) and human electro/magneto-encephalogram (EEG/MEG) studies. In addition, EEG studies often use flickering stimuli that produce a specific measure called steady-state-visually-evoked-potential (SSVEP), whose power also increases with attention. However, effectiveness of these neural measures in capturing attentional modulation is unknown since stimuli and task paradigms vary widely across studies. In a recent macaque study, attentional modulation was more salient in the gamma band of the LFP, compared to alpha or SSVEP. To compare this with human EEG, we designed an orientation change detection task where we presented both static and counterphasing stimuli of matched difficulty levels to 26 subjects and compared attentional modulation of various measures under similar conditions. We report two main results. First, attentional modulation was comparable for SSVEP and alpha. Second, non-foveal stimuli produced weak gamma despite various stimulus optimizations and showed negligible attentional modulation although full-screen gratings showed robust gamma activity. Our results are useful for brain-machine-interfacing studies where suitable features are used for decoding attention, and also provide clues about spatial scales of neural mechanisms underlying attention.

RevDate: 2023-11-10

Wu L, Wang J, Lu Y, et al (2023)

Association of intimate partner violence with offspring growth in 32 low- and middle-income countries: a population-based cross-sectional study.

Archives of women's mental health [Epub ahead of print].

Intimate partner violence (IPV) against women presents a major public health challenge, especially in low-income and middle-income countries (LMICs), and its relationship with poor offspring growth is emerging but remains understudied. This study aimed to explore the impact of maternal exposure to IPV on offspring growth based on different approaches in LMICs. We conducted a population-based cross-sectional study using the most recent Demographic and Health Surveys from 32 LMICs; 81,652 mother-child dyads comprising women aged from 15 to 49 years with children aged 0 to 59 months were included. We applied logistic regression models to explore the independent and cumulative relationship between IPV, including emotional, physical, and sexual IPV, with poor child growth status, including stunting and wasting; 52.6% of mothers were under the age of 30 years with a 36% prevalence of any lifetime exposure to IPV. Maternal exposure to any IPV increased the odds of stunting, but only physical and sexual IPV were independently associated with an increased risk of stunting. Three different types of IPV exhibited a cumulative effect on stunting. Maternal exposure to physical IPV was significantly associated with an increased risk of wasting. Significant associations between maternal exposure to emotional IPV with offspring stunting and physical IPV with wasting were only observed in children aged 0 to 36 months. IPV against women remains high in LMICs and has adverse effects on offspring growth. Policy and program efforts are needed to prioritize the reduction of widespread physical and sexual IPV and to mitigate the impact of such violence.

RevDate: 2023-11-09

Levett JJ, Elkaim LM, Niazi F, et al (2023)

Invasive Brain Computer Interface for Motor Restoration in Spinal Cord Injury: A Systematic Review.

Neuromodulation : journal of the International Neuromodulation Society pii:S1094-7159(23)00754-7 [Epub ahead of print].

STUDY DESIGN: Systematic review of the literature.

OBJECTIVES: In recent years, brain-computer interface (BCI) has emerged as a potential treatment for patients with spinal cord injury (SCI). This is the first systematic review of the literature on invasive closed-loop BCI technologies for the treatment of SCI in humans.

MATERIALS AND METHODS: A comprehensive search of PubMed MEDLINE, Web of Science, and Ovid EMBASE was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

RESULTS: Of 8316 articles collected, 19 studies met all the inclusion criteria. Data from 21 patients were extracted from these studies. All patients sustained a cervical SCI and were treated using either a BCI with intracortical microelectrode arrays (n = 18, 85.7%) or electrocorticography (n = 3, 14.3%). To decode these neural signals, machine learning and statistical models were used: support vector machine in eight patients (38.1%), linear estimator in seven patients (33.3%), Hidden Markov Model in three patients (14.3%), and other in three patients (14.3%). As the outputs, ten patients (47.6%) underwent noninvasive functional electrical stimulation (FES) with a cuff; one (4.8%) had an invasive FES with percutaneous stimulation, and ten (47.6%) used an external device (neuroprosthesis or virtual avatar). Motor function was restored in all patients for each assigned task. Clinical outcome measures were heterogeneous across all studies.

CONCLUSIONS: Invasive techniques of BCI show promise for the treatment of SCI, but there is currently no technology that can restore complete functional autonomy in patients with SCI. The current techniques and outcomes of BCI vary greatly. Because invasive BCIs are still in the early stages of development, further clinical studies should be conducted to optimize the prognosis for patients with SCI.

RevDate: 2023-11-10

Duan D, Wu Z, Zhou Y, et al (2023)

Working memory training and evaluation based on brain-computer interface and virtual reality: our opinion.

Frontiers in human neuroscience, 17:1291983.

RevDate: 2023-11-08

Qin C, Yuan Q, Liu M, et al (2023)

Biohybrid tongue based on hypothalamic neuronal network-on-a-chip for real-time blood glucose sensing and assessment.

Biosensors & bioelectronics, 244:115784 pii:S0956-5663(23)00726-1 [Epub ahead of print].

The expression of sweet receptors in the hypothalamus has been implicated in energy homeostasis control and the pathogenesis of obesity and diabetes. However, the exact mechanism by which hypothalamic glucose-sensing neurons function remains unclear. Conventional detection methods, such as fiber photometry, optogenetics, brain-machine interfaces, patch clamp and calcium imaging, pose limitations for real-time glucose perception due to their complexity, cytotoxicity and so on. Therefore, this study proposes a biohybrid tongue based on hypothalamic neuronal network (HNN)-on-a-chip coupling with microelectrode array (MEA) for real-time glucose perception. Hypothalamic neuronal cultures were cultivated on a two-dimensional "brain-on-chip" device, enabling the formation of neuronal networks and electrophysiological signal detection. Additionally, we investigated the endogenous expression of sweet taste receptors (T1R2/T1R3) in hypothalamic neuronal cells, providing the basis for the biohybrid tongue based on HNN-on-a-chip's sweetness detection capabilities. The spike signal response to sucrose and glucose stimulation was detected, and concentration-dependent responses were explored with glucose concentrations ranging from 0.01 mM to 8 mM. MEAs allow for real-time recordings, enabling the observation of dynamic changes in neuronal responses to glucose fluctuations over time. The biohybrid tongue based on HNN-on-a-chip can measure various parameters, including spike frequency and amplitude, providing insights into neuronal firing patterns and excitability. Moreover, hypothalamic glucoregulatory neurons that sense and respond to changes in blood glucose was identified, including glucose-excited neurons (GE-Neurons) and glucose-inhibited neurons (GI-Neurons). The detection range for GE-Neurons spans from 0.4 to 6 mM, while GI-Neurons demonstrate sensitivity within the range of 1-8 mM. And the glucose detection limit was firmly established at 0.01 mM. Through non-linear regression analysis, the IC50 for GI-Neurons' spike firing was determined to be 4.18 mM. In conclusion, the biohybrid tongue based on HNN-on-a-chip offers a valuable in vitro tool for studying hypothalamic neurons, elucidating glucose sensing mechanisms, and understanding hypothalamic neuronal function.

RevDate: 2023-11-08

Eskandari R, M Sawan (2023)

Challenges and Perspectives on Impulse Radio-Ultra-Wideband Transceivers for Neural Recording Applications.

IEEE transactions on biomedical circuits and systems, PP: [Epub ahead of print].

Brain-machine interfaces (BMI) are widely adopted in neuroscience investigations and neural prosthetics, with sensing channel counts constantly increasing. These Investigations place increasing demands for high data rates and low-power implantable devices despite high tissue losses. The Impulse radio ultra- wideband (IR-UWB), a revived wireless technology for short-range radios, has been widely used in various applications. Since the requirements and solutions are application-oriented, in this review paper we focus on neural recording implants with high-data rates and ultra-low power requirements. We examine in detail the working principle, design methodology, performance, and implementations of different architectures of IR-UWB transceivers in a quantitative manner to draw a deep comparison and extract the bottlenecks and possible solutions concerning the dedicated application. Our analysis shows that current solutions rely on enhanced or combined modulation techniques to improve link margin. An in-depth study of prior-art publications that achieved Gbps data rates concludes that edge-combination architecture and non-coherent detectors are remarkable for transmitter and receiver, respectively. Although the aim to minimize power and improve data rate - defined as energy efficiency (pJ/b) - extending communication distance despite high tissue losses and limited power budget, good narrow-band interference (NBI) tolerance coexisted in the same frequency band of UWB systems, and compatibility with energy harvesting designs are among the critical challenges remained unsolved. Furthermore, we expect that the combination of artificial intelligence (AI) and the inherent advantages of UWB radios will pave the way for future improvements in BMIs.

RevDate: 2023-11-09
CmpDate: 2023-11-09

Drew L (2023)

The rise of brain-reading technology: what you need to know.

Nature, 623(7986):241-243.

RevDate: 2023-11-08

Liu M, Li T, Zhang X, et al (2023)

IMH-Net: a convolutional neural network for end-to-end EEG motor imagery classification.

Computer methods in biomechanics and biomedical engineering [Epub ahead of print].

As the main component of Brain-computer interface (BCI) technology, the classification algorithm based on EEG has developed rapidly. The previous algorithms were often based on subject-dependent settings, resulting in BCI needing to be calibrated for new users. In this work, we propose IMH-Net, an end-to-end subject-independent model. The model first uses Inception blocks extracts the frequency domain features of the data, then further compresses the feature vectors to extract the spatial domain features, and finally learns the global information and classification through Multi-Head Attention mechanism. On the OpenBMI dataset, IMH-Net obtained 73.90 ± 13.10% accuracy and 73.09 ± 14.99% F1-score in subject-independent manner, which improved the accuracy by 1.96% compared with the comparison model. On the BCI competition IV dataset 2a, this model also achieved the highest accuracy and F1-score in subject-dependent manner. The IMH-Net model we proposed can improve the accuracy of subject-independent Motor Imagery (MI), and the robustness of the algorithm is high, which has strong practical value in the field of BCI.

RevDate: 2023-11-08

Pastötter B, C Frings (2023)

Prestimulus alpha power signals attention to retrieval.

The European journal of neuroscience [Epub ahead of print].

The human brain is in distinct processing modes at different times. Specifically, a distinction can be made between encoding and retrieval modes, which refer to the brain's state when it is storing new information or searching for old information, respectively. Recent research proposed the idea of a "ready-to-encode" mode, which describes a prestimulus effect in brain activity that signals (external) attention to encoding and predicts subsequent memory performance. Whether there is also a corresponding "ready-to-retrieve" mode in human brain activity is currently unclear. In this study, we examined whether prestimulus oscillations can be linked to (internal) attention to retrieval. We show that task cues to prepare for retrieval (or testing) in comparison with restudy of previously studied vocabulary word pairs led to a significant decrease of prestimulus alpha power just before the onset of word stimuli. Beamformer analysis localized this effect in the right secondary visual cortex (Brodmann area 18). Correlation analysis showed that the task cue-induced, prestimulus alpha power effect is positively related to stimulus-induced alpha/beta power, which in turn predicted participants' memory performance. The results are consistent with the idea that prestimulus alpha power signals internal attention to retrieval, which promotes the elaborative processing of episodic memories. Future research on brain-computer interfaces may find the findings interesting regarding the potential of using online measures of fluctuating alpha oscillations to trigger the presentation and sequencing of restudy and testing trials, ultimately enhancing instructional learning strategies.

RevDate: 2023-11-10

Yu H, Ni P, Tian Y, et al (2023)

Association of elevated levels of peripheral complement components with cortical thinning and impaired logical memory in drug-naïve patients with first-episode schizophrenia.

Schizophrenia (Heidelberg, Germany), 9(1):79.

Schizophrenia has been linked to polymorphism in genes encoding components of the complement system, and hyperactive complement activity has been linked to immune dysfunction in schizophrenia patients. Whether and how specific complement components influence brain structure and cognition in the disease is unclear. Here we compared 52 drug-naïve patients with first-episode schizophrenia and 52 healthy controls in terms of levels of peripheral complement factors, cortical thickness (CT), logical memory and psychotic symptoms. We also explored the relationship between complement factors with CT, cognition and psychotic symptoms. Patients showed significantly higher levels of C1q, C4, factor B, factor H, and properdin in plasma. Among patients, higher levels of C3 in plasma were associated with worse memory recall, while higher levels of C4, factor B and factor H were associated with thinner sensory cortex. These findings link dysregulation of specific complement components to abnormal brain structure and cognition in schizophrenia.

RevDate: 2023-11-08

Park D, Park H, Kim S, et al (2023)

Spatio-temporal explanation of 3D-EEGNet for motor imagery EEG classification using permutation and saliency.

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

Recently, convolutional neural network (CNN)-based classification models have shown good performance for motor imagery (MI) brain-computer interfaces (BCI) using electroencephalogram (EEG) in end-to-end learning. Although a few explainable artificial intelligence (XAI) techniques have been developed, it is still challenging to interpret the CNN models for EEG-based BCI classification effectively. In this research, we propose 3D-EEGNet as a 3D CNN model to improve both the explainability and performance of MI EEG classification. The proposed approach exhibited better performances on two MI EEG datasets than the existing EEGNet, which uses a 2D input shape. The MI classification accuracies are improved around 1.8% and 6.1% point in average on the datasets, respectively. The permutation-based XAI method is first applied for the reliable explanation of the 3D-EEGNet. Next, to find a faster XAI method for spatio-temporal explanation, we design a novel technique based on the normalized discounted cumulative gain (NDCG) for selecting the best among a few saliency-based methods due to their higher time complexity than the permutation-based method. Among the saliency-based methods, DeepLIFT was selected because the NDCG scores indicated its results are the most similar to the permutation-based results. Finally, the fast spatio-temporal explanation using DeepLIFT provides deeper understanding for the classification results of the 3D-EEGNet and the important properties in the MI EEG experiments.

RevDate: 2023-11-07

Wang Z, Fang J, J Zhang (2023)

Rethinking Delayed Hemodynamic Responses for fNIRS Classification.

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

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technology for monitoring cerebral hemodynamic responses. Enhancing fNIRS classification can improve the performance of brain-computer interfaces (BCIs). Currently, deep neural networks (DNNs) do not consider the inherent delayed hemodynamic responses of fNIRS signals, which causes many optimization and application problems. Considering the kernel size and receptive field of convolutions, delayed hemodynamic responses as domain knowledge are introduced into fNIRS classification, and a concise and efficient model named fNIRSNet is proposed. We empirically summarize three design guidelines for fNIRSNet. In subject-specific and subject-independent experiments, fNIRSNet outperforms other DNNs on open-access datasets. Specifically, fNIRSNet with only 498 parameters is 6.58% higher than convolutional neural network (CNN) with millions of parameters on mental arithmetic tasks and the floating-point operations (FLOPs) of fNIRSNet are much lower than CNN. Therefore, fNIRSNet is friendly to practical applications and reduces the hardware cost of BCI systems. It may inspire more research on knowledge-driven models for fNIRS BCIs. Code is available at

RevDate: 2023-11-06

Ma S, Chen M, Jiang Y, et al (2023)

Author Correction: Sustained antidepressant effect of ketamine through NMDAR trapping in the LHb.

RevDate: 2023-11-09
CmpDate: 2023-11-08

Duraivel S, Rahimpour S, Chiang CH, et al (2023)

High-resolution neural recordings improve the accuracy of speech decoding.

Nature communications, 14(1):6938.

Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse neural recordings which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed high-resolution, micro-electrocorticographic (µECoG) neural recordings during intra-operative speech production. We obtained neural signals with 57× higher spatial resolution and 48% higher signal-to-noise ratio compared to macro-ECoG and SEEG. This increased signal quality improved decoding by 35% compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than linear techniques. We show that high-density µECoG can enable high-quality speech decoding for future neural speech prostheses.

RevDate: 2023-11-06

Han J, Wei X, AA Faisal (2023)

EEG decoding for datasets with heterogenous electrode configurations using transfer learning graph neural networks.

Journal of neural engineering [Epub ahead of print].

Objective Brain-Machine Interfacing (BMI) has greatly benefited from adopting machine learning methods for feature learning that require extensive data for training, which are often unavailable from a single dataset. Yet, it is difficult to combine data across labs or even data within the same lab collected over the years due to the variation in recording equipment and electrode layouts resulting in shifts in data distribution, changes in data dimensionality, and altered identity of data dimensions. Our objective is to overcome this limitation and learn from many different and diverse datasets across labs with different experimental protocols. Approach To tackle the domain adaptation problem, we developed a novel machine learning framework combining graph neural networks (GNNs) and transfer learning methodologies for non-invasive Motor Imagery (MI) EEG decoding, as an example of BMI. Empirically, we focus on the challenges of learning from EEG data with different electrode layouts and varying numbers of electrodes. We utilise three MI EEG databases collected using very different numbers of EEG sensors (from 22 channels to 64) and layouts (from custom layouts to 10-20). Main Results Our model achieved the highest accuracy with lower standard deviations on the testing datasets. This indicates that the GNN-based transfer learning framework can effectively aggregate knowledge from multiple datasets with different electrode layouts, leading to improved generalization in subject-independent MI EEG classification. Significance The findings of this study have important implications for Brain-Computer-Interface (BCI) research, as they highlight a promising method for overcoming the limitations posed by non-unified experimental setups. By enabling the integration of diverse datasets with varying electrode layouts, our proposed approach can help advance the development and application of BMI technologies.

RevDate: 2023-11-06

Pan LC, Wang K, Xu L, et al (2023)

Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Brain-computer interfaces (BCIs) enable a direct communication pathway between the human brain and external devices, without relying on the traditional peripheral nervous and musculoskeletal systems. Motor imagery (MI)-based BCIs have attracted significant interest for their potential in motor rehabilitation. However, current algorithms fail to account for the cross-session variability of electroencephalography (EEG) signals, limiting their practical application.

APPROACH: We proposed a Riemannian geometry-based adaptive boosting and voting ensemble (RAVE) algorithm to address this issue. Our approach segmented the MI period into multiple sub-datasets using a sliding window approach and extracted features from each sub-dataset using Riemannian geometry. We then trained adaptive boosting (AdaBoost) ensemble learning classifiers for each sub-dataset, with the final BCI output determined by majority voting of all classifiers. We tested our proposed RAVE algorithm and eight other competing algorithms on four datasets (Pan2023, BNCI001-2014, BNCI001-2015, BNCI004-2015).

MAIN RESULTS: Our results showed that, in the cross-session scenario, the RAVE algorithm outperformed the eight other competing algorithms significantly under different within-session training sample sizes. On most datasets (Pan2023, BNCI001-2014, BNCI001-2015), the RAVE algorithm could achieve or even exceed the classification performance of traditional algorithms that used a large amount of training samples, even when it did not require or only required a small amount of within-session training samples.

SIGNIFICANCE: These findings indicate that our cross-session decoding strategy could enable MI-BCI applications that require no or minimal training process.

RevDate: 2023-11-10
CmpDate: 2023-11-10

Xu G, Wang Z, Zhao X, et al (2023)

Attentional State Classification Using Amplitude and Phase Feature Extraction Method Based on Filter Bank and Riemannian Manifold.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 31:4402-4412.

As a significant aspect of cognition, attention has been extensively studied and numerous measurements have been developed based on brain signal processing. Although existing attentional state classification methods have achieved good accuracy by extracting a variety of handcrafted features, spatial features have not been fully explored. This paper proposes an attentional state classification method based on Riemannian manifold to utilize spatial information. Based on the concept of Riemannian manifold of symmetric positive definite (SPD) matrix, the proposed method exploits the structure of covariance matrix to extract spatial features instead of using spatial filters. Specifically, Riemannian distances from intra-class Riemannian means are extracted as features for their robustness. To fully extend the potential of electroencephalograph (EEG) signal, both amplitude and phase information is utilized. In addition, to solve the variance of frequency bands, a filter bank is employed to process the signal of different frequency bands separately. Finally, features are fed into a support vector machine with a polynomial kernel to obtain classification results. The proposed attentional state classification using amplitude and phase feature extraction method based on filter bank and Riemannian manifold (AP-FBRM) method is evaluated on two open datasets including EEG data of 29 and 26 subjects. According to the experimental results, the optimal set of filter bank and the optimal technique to extract features containing both amplitude and phase information are determined. The proposed method respectively achieves accuracies of 88.06% and 80.00% and outperforms 8 baseline methods, which manifests that the proposed method creates an efficient way to recognize attentional state.

RevDate: 2023-11-10

Yan Y, Zhou P, Ding L, et al (2023)

T Cell Antigen Recognition and Discrimination by Electrochemiluminescence Imaging.

Angewandte Chemie (International ed. in English) [Epub ahead of print].

Adoptive T lymphocyte (T cell) transfer and tumour-specific peptide vaccines are innovative cancer therapies. An accurate assessment of the specific reactivity of T cell receptors (TCRs) to tumour antigens is required because of the high heterogeneity of tumour cells and the immunosuppressive tumour microenvironment. In this study, we report a label-free electrochemiluminescence (ECL) imaging approach for recognising and discriminating between TCRs and tumour-specific antigens by imaging the immune synapses of T cells. Various T cell stimuli, including agonistic antibodies, auxiliary molecules, and tumour-specific antigens, were modified on the electrode's surface to allow for their interaction with T cells bearing different TCRs. The formation of immune synapses activated by specific stimuli produced a negative (shadow) ECL image, from which T cell antigen recognition and discrimination were evaluated by analysing the spreading area and the recognition intensity of T cells. This approach provides an easy way to assess TCR-antigen specificity and screen both of them for immunotherapies.

RevDate: 2023-11-06

Wang J, Bi L, W Fei (2023)

EEG-Based Motor BCIs for Upper Limb Movement: Current Techniques and Future Insights.

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

Motor brain-computer interface (BCI) refers to the BCI that decodes voluntary motion intentions from brain signals directly and outputs corresponding control commands without activating peripheral nerves and muscles. Motor BCIs can be used for the restoration, compensation, and augmentation of motor function by activating the neuromuscular circuit and facilitating neural plasticity. The essential applications of motor BCIs include neurorehabilitation and daily-life assistance for motor-impaired patients. In recent years, studies on motor BCIs mainly concentrate on neural signatures, movement decoding, and its applications. In this review, we aim to provide a comprehensive review of the state-of-the-art research of electroencephalography (EEG) signals-based motor BCIs for the first time. We also aim to give some insights into advancing motor BCIs to a more natural and practical application scenario. In particular, we focus on the motor BCIs for the movements of the upper limbs. Specifically, the experimental paradigms, techniques, and application systems of upper-limb BCIs are reviewed. Several vital issues in developing more natural and practical upper-limb motor BCIs, including developing target-users-oriented, distraction-robust, and multi-limbs motor BCIs, and applying fusion techniques to promote the natural and practical motor BCIs, are discussed.

RevDate: 2023-11-07

Tong L, Qian Y, Peng L, et al (2023)

A learnable EEG channel selection method for MI-BCI using efficient channel attention.

Frontiers in neuroscience, 17:1276067.

INTRODUCTION: During electroencephalography (EEG)-based motor imagery-brain-computer interfaces (MI-BCIs) task, a large number of electrodes are commonly used, and consume much computational resources. Therefore, channel selection is crucial while ensuring classification accuracy.

METHODS: This paper proposes a channel selection method by integrating the efficient channel attention (ECA) module with a convolutional neural network (CNN). During model training process, the ECA module automatically assigns the channel weights by evaluating the relative importance for BCI classification accuracy of every channel. Then a ranking of EEG channel importance can be established so as to select an appropriate number of channels to form a channel subset from the ranking. In this paper, the ECA module is embedded into a commonly used network for MI, and comparative experiments are conducted on the BCI Competition IV dataset 2a.

RESULTS AND DISCUSSION: The proposed method achieved an average accuracy of 75.76% with all 22 channels and 69.52% with eight channels in a four-class classification task, outperforming other state-of-the-art EEG channel selection methods. The result demonstrates that the proposed method provides an effective channel selection approach for EEG-based MI-BCI.

RevDate: 2023-11-07

Vorreuther A, Bastian L, Benitez Andonegui A, et al (2023)

It takes two (seconds): decreasing encoding time for two-choice functional near-infrared spectroscopy brain-computer interface communication.

Neurophotonics, 10(4):045005.

SIGNIFICANCE: Brain-computer interfaces (BCIs) can provide severely motor-impaired patients with a motor-independent communication channel. Functional near-infrared spectroscopy (fNIRS) constitutes a promising BCI-input modality given its high mobility, safety, user comfort, cost-efficiency, and relatively low motion sensitivity.

AIM: The present study aimed at developing an efficient and convenient two-choice fNIRS communication BCI by implementing a relatively short encoding time (2 s), considerably increasing communication speed, and decreasing the cognitive load of BCI users.

APPROACH: To encode binary answers to 10 biographical questions, 10 healthy adults repeatedly performed a combined motor-speech imagery task within 2 different time windows guided by auditory instructions. Each answer-encoding run consisted of 10 trials. Answers were decoded during the ongoing experiment from the time course of the individually identified most-informative fNIRS channel-by-chromophore combination.

RESULTS: The answers of participants were decoded online with an accuracy of 85.8% (run-based group mean). Post-hoc analysis yielded an average single-trial accuracy of 68.1%. Analysis of the effect of number of trial repetitions showed that the best information-transfer rate could be obtained by combining four encoding trials.

CONCLUSIONS: The study demonstrates that an encoding time as short as 2 s can enable immediate, efficient, and convenient fNIRS-BCI communication.

RevDate: 2023-11-09

Zhang H, Zhang Y, Wang X, et al (2023)

Transcranial dipole localization and decoding study based on ultrasonic phased array for acoustoelectric brain imaging.

Journal of neural engineering, 20(6):.

Objective. Neuroimaging is one of the effective tools to understand the functional activities of the brain, but traditional non-invasive neuroimaging techniques are difficult to combine both high temporal and spatial resolution to satisfy clinical needs. Acoustoelectric brain imaging (ABI) can combine the millimeter spatial resolution advantage of focused ultrasound with the millisecond temporal resolution advantage of electroencephalogram signals.Approach. In this study, we first explored the transcranial modulated acoustic field distribution based on ABI, and further localized and decoded single and double dipoles signals.Main results. The results show that the simulation-guided acoustic field modulation results are significantly better than those of self-focusing, which can realize precise modulation focusing of intracranial target focusing. The single dipole transcranial localization error is less than 0.4 mm and the decoding accuracy is greater than 0.93. The double dipoles transcranial localization error is less than 0.2 mm and the decoding accuracy is greater than 0.89.Significance. This study enables precise focusing of transcranial acoustic field modulation, high-precision localization of source signals and decoding of their waveforms, which provides a technical method for ABI in localizing evoked excitatory neuron areas and epileptic focus.

RevDate: 2023-11-07

Graham B, A Ehlers (2023)

Development and Validation of the Bullied Cognitions Inventory (BCI).

Cognitive therapy and research, 47(6):1033-1045.

BACKGROUND: Bullying increases risk of social anxiety and can produce symptoms of posttraumatic stress disorder (PTSD). According to cognitive models, these are maintained by unhelpful beliefs, which are therefore assessed and targeted in cognitive therapy. This paper describes psychometric validation of a new measure of beliefs related to bullying experiences.

METHODS: In an online survey of 1879 young people before starting university or college in the UK, 1279 reported a history of bullying (N = 1279), and 854 rated their agreement with beliefs about self and others related to bullying experiences and completed symptom measures of social anxiety and PTSD related to bullying. An empirical structure for a Bullied Cognitions Inventory was established using exploratory and confirmatory factor analyses and assessed using model fit statistics and tests of reliability and validity.

RESULTS: Fifteen items clustered into four themes: "degraded in the eyes of others", "negative interpretations of reactions to bullying", "recognisable as a bullying victim" and "social defeat". The measure has acceptable reliability and validity and, accounting for existing cognitive measures, explained additional variance in symptoms of PTSD but not social anxiety.

CONCLUSIONS: The Bullied Cognitions Inventory (BCI) is a valid and reliable tool for measuring cognitions related to bullying. It may be useful in therapy for identifying and monitoring unhelpful cognitions in those who were bullied.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10608-023-10412-6.

RevDate: 2023-11-08
CmpDate: 2023-11-07

Zhang X, Wang W, Bai X, et al (2023)

Increased glymphatic system activity in migraine chronification by diffusion tensor image analysis along the perivascular space.

The journal of headache and pain, 24(1):147.

BACKGROUND: Preliminary evidence suggests that several headache disorders may be associated with glymphatic dysfunction. However, no studies have been conducted to examine the glymphatic activity in migraine chronification.

PURPOSES: To investigate the glymphatic activity of migraine chronification in patients with episodic migraine (EM) and chronic migraine (CM) using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) method.

METHODS: In this cross-sectional study, patients with EM, CM, and healthy controls (HCs) were included. All participants underwent a standard brain magnetic resonance imaging (MRI) examination. Bilateral DTI-ALPS indexes were calculated for all participants and compared among EM, CM, and HC groups. Correlations between the DTI-ALPS index and clinical characteristics were analyzed.

RESULTS: A total of 32 patients with EM, 24 patients with CM, and 41 age- and sex-matched HCs were included in the analysis. Significant differences were found in the right DTI-ALPS index among the three groups (p = 0.011), with CM showing significantly higher values than EM (p = 0.033) and HCs (p = 0.015). The right DTI-ALPS index of CM group was significantly higher than the left DTI-ALPS index (p = 0.005). And the headache intensity was correlated to DTI-ALPS index both in the left hemisphere (r = 0.371, p = 0.011) and in the right hemisphere (r = 0.307, p = 0.038), but there were no correlations after Bonferroni correction.

CONCLUSIONS: Glymphatic system activity is shown to be increased instead of impaired during migraine chronification. The mechanism behind this observation suggests that increased glymphatic activity is more likely to be a concomitant phenomenon of altered vascular reactivity associated with migraine pathophysiology rather than a risk factor of migraine chronification.

RevDate: 2023-11-07

Comandini G, Ouisse M, Ting VP, et al (2023)

Acoustic transmission loss in Hilbert fractal metamaterials.

Scientific reports, 13(1):19058.

Acoustic metamaterials are increasingly being considered as a viable technology for sound insulation. Fractal patterns constitute a potentially groundbreaking architecture for acoustic metamaterials. We describe in this work the behaviour of the transmission loss of Hilbert fractal metamaterials used for sound control purposes. The transmission loss of 3D printed metamaterials with Hilbert fractal patterns related to configurations from the zeroth to the fourth order is investigated here using impedance tube tests and Finite Element models. We evaluate, in particular, the impact of the equivalent porosity and the relative size of the cavity of the fractal pattern versus the overall dimensions of the metamaterial unit. We also provide an analytical formulation that relates the acoustic cavity resonances in the fractal patterns and the frequencies associated with the maxima of the transmission losses, providing opportunities to tune the sound insulation properties through control of the fractal architecture.

RevDate: 2023-11-04

Pak S, Lee M, Lee S, et al (2023)

Cortical surface plasticity promotes map remodeling and alleviates tinnitus in adult mice.

Progress in neurobiology pii:S0301-0082(23)00144-2 [Epub ahead of print].

Tinnitus induced by hearing loss is caused primarily by irreversible damage to the peripheral auditory system, which results in abnormal neural responses and frequency map disruption in the central auditory system. It remains unclear whether and how electrical rehabilitation of the auditory cortex can alleviate tinnitus. We hypothesize that stimulation of the cortical surface can alleviate tinnitus by enhancing neural responses and promoting frequency map reorganization. To test this hypothesis, we assessed and activated cortical maps using our newly designed graphene-based electrode array with a noise-induced tinnitus animal model. We found that cortical surface stimulation increased cortical activity, reshaped sensory maps, and alleviated hearing loss-induced tinnitus behavior in adult mice. These effects were likely due to retained long-term synaptic potentiation capabilities, as shown in cortical slices from the mice model. These findings suggest that cortical surface activation can be used to facilitate practical functional recovery from phantom percepts induced by sensory deprivation. They also provide a working principle for various treatment methods that involve electrical rehabilitation of the cortex.

RevDate: 2023-11-06
CmpDate: 2023-11-06

Ng TTW, Davel S, KD O'Connor (2023)

Sulfasalazine-Induced Delayed Hypersensitivity Reaction Presenting as Fever, Aseptic Meningitis, and Mesenteric Panniculitis in a Patient with Seronegative Arthritis.

The American journal of case reports, 24:e941623.

BACKGROUND An 82-year-old woman presented with acute pyrexial illness and mesenteric panniculitis and developed biochemical aseptic meningitis (cerebrospinal fluid pleocytosis with no identifiable pathogen). Investigation determined her illness was likely a delayed hypersensitivity reaction caused by sulfasalazine. Sulfasalazine-induced aseptic meningitis is a rare condition often diagnosed late in a patient's admission owing to initial non-specific illness symptomatology requiring the exclusion of more common "red flag" etiologies, such as infection and malignancy. CASE REPORT An 82-year-old woman with a history of recurrent urinary tract infections and seronegative arthritis presented with a 3-day history of fatigue, headache, dyspnea, and lassitude. On admission, she was treated as presumed sepsis of uncertain source owing to pyrexia and tachycardia. Brain computer tomography (CT) revealed no acute intracranial abnormality. Furthermore, CT of the chest, abdomen, and pelvis did not reveal any source of sepsis or features of malignancy. After excluding infective etiologies with serological and cerebrospinal fluid testing, sulfasalazine-induced aseptic meningitis (SIAM) was diagnosed. The patient was then commenced on intravenous steroids, resulting in immediate defervescence and symptom resolution. CONCLUSIONS SIAM remains a diagnostic challenge since patients present with non-specific signs and symptoms, such as pyrexia, headaches, and lassitude. These patients require a thorough investigative battery starting with anamnesis, physical examination, biochemical testing, and radiologic imaging. This case illustrates the need for a high suspicion index of drug-induced hypersensitivity reaction in a rheumatological patient with pyrexial illness where infective etiologies have been confidently excluded. Prompt initiation of intravenous steroids in SIAM provides a dramatic recovery and resolution of symptoms.

RevDate: 2023-11-03

Gao K, Hu M, Li J, et al (2023)

Drug-detecting bioelectronic nose based on odor cue memory combined with a brain computer interface.

Biosensors & bioelectronics, 244:115797 pii:S0956-5663(23)00739-X [Epub ahead of print].

The international drug situation is increasingly, various new drugs are hidden in public places through changing forms and packaging, which brings new challenges to drug enforcement. This study proposes a drug-detecting bioelectronic nose based on odor cue memory combined with brain-computer interface and optogenetic regulation technologies. First, the rats were trained to generate positive memories of drug odors through food reward training, and multichannel microelectrodes were implanted into the DG region of the hippocampus for responsible memory retrieval, the spike signals of individual neurons and the local field potential signals of population neurons in the brain region were collected for pattern recognition and analysis. Preliminary experimental results have shown that when low-dose drugs are buried in a hidden area, rats can find the location of the drugs in a very short time, and when close to the relevant area, there is a significant change in the energy value and time-frequency spectrum signal coupling of the returned data, which can be extracted to indicate that the rats have found the drugs. Second, we labled the neuronal activity marker c-fos and revealed more robust activation in the DG region following odor detection. We modulated these neurons through neuroregulatory technology, so that the rats could recognize drugs by retrieving memories more quickly. We conceive that the drug-detecting rat robot can detect trace amounts of various drugs in complex terrain and multiple scenes, which is of great significance for anti-drug work in the future.

RevDate: 2023-11-04

Sankaran N, Moses D, Chiong W, et al (2023)

Recommendations for promoting user agency in the design of speech neuroprostheses.

Frontiers in human neuroscience, 17:1298129.

Brain-computer interfaces (BCI) that directly decode speech from brain activity aim to restore communication in people with paralysis who cannot speak. Despite recent advances, neural inference of speech remains imperfect, limiting the ability for speech BCIs to enable experiences such as fluent conversation that promote agency - that is, the ability for users to author and transmit messages enacting their intentions. Here, we make recommendations for promoting agency based on existing and emerging strategies in neural engineering. The focus is on achieving fast, accurate, and reliable performance while ensuring volitional control over when a decoder is engaged, what exactly is decoded, and how messages are expressed. Additionally, alongside neuroscientific progress within controlled experimental settings, we argue that a parallel line of research must consider how to translate experimental successes into real-world environments. While such research will ultimately require input from prospective users, here we identify and describe design choices inspired by human-factors work conducted in existing fields of assistive technology, which address practical issues likely to emerge in future real-world speech BCI applications.

RevDate: 2023-11-04

Schmoigl-Tonis M, Schranz C, GR Müller-Putz (2023)

Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review.

Frontiers in human neuroscience, 17:1251690.

Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.

RevDate: 2023-11-04

Sebastián-Romagosa M, Cho W, Ortner R, et al (2023)

Brain-computer interface treatment for gait rehabilitation in stroke patients.

Frontiers in neuroscience, 17:1256077.

The use of Brain-Computer Interfaces (BCI) as rehabilitation tools for chronically ill neurological patients has become more widespread. BCIs combined with other techniques allow the user to restore neurological function by inducing neuroplasticity through real-time detection of motor-imagery (MI) as patients perform therapy tasks. Twenty-five stroke patients with gait disability were recruited for this study. Participants performed 25 sessions with the MI-BCI and assessment visits to track functional changes during the therapy. The results of this study demonstrated a clinically significant increase in walking speed of 0.19 m/s, 95%CI [0.13-0.25], p < 0.001. Patients also reduced spasticity and improved their range of motion and muscle contraction. The BCI treatment was effective in promoting long-lasting functional improvements in the gait speed of chronic stroke survivors. Patients have more movements in the lower limb; therefore, they can walk better and safer. This functional improvement can be explained by improved neuroplasticity in the central nervous system.

RevDate: 2023-11-05

Ke Y, Liu S, Chen L, et al (2023)

Lasting enhancements in neural efficiency by multi-session transcranial direct current stimulation during working memory training.

NPJ science of learning, 8(1):48.

The neural basis for long-term behavioral improvements resulting from multi-session transcranial direct current stimulation (tDCS) combined with working memory training (WMT) remains unclear. In this study, we used task-related electroencephalography (EEG) measures to investigate the lasting neurophysiological effects of anodal high-definition (HD)-tDCS applied over the left dorsolateral prefrontal cortex (dlPFC) during a challenging WMT. Thirty-four healthy young adults were randomized to sham or active tDCS groups and underwent ten 30-minute training sessions over ten consecutive days, preceded by a pre-test and followed by post-tests performed one day and three weeks after the last session, respectively, by performing high-load WM tasks along with EEG recording. Multi-session HD-tDCS significantly enhanced the behavioral benefits of WMT. Compared to the sham group, the active group showed facilitated increases in theta, alpha, beta, and gamma task-related oscillations at the end of training and significantly increased P300 response 3 weeks post-training. Our findings suggest that applying anodal tDCS over the left dlPFC during multi-session WMT can enhance the behavioral benefits of WMT and facilitate sustained improvements in WM-related neural efficiency.

RevDate: 2023-11-02

Myhrum M, Heldahl MG, Rødvik AK, et al (2023)

Validation of the Norwegian Version of the Speech, Spatial and Qualities of Hearing Scale (SSQ).

Audiology & neuro-otology pii:000534197 [Epub ahead of print].

INTRODUCTION: The main objective of the study was to validate the Norwegian translation of the Speech, Spatial and Qualities of Hearing Scale (SSQ) and investigate the SSQ disability profiles in a cochlear implant (CI) user population.

METHODS: The study involved 152 adult CI users. The mean age at implantation was 55 (standard deviation [SD] = 16), and the mean CI experience was 5 years (SD = 4.8). The cohort was split into three groups depending on the hearing modality: bilateral CIs (BCIs), a unilateral CI (UCI), and bimodal (CI plus contralateral hearing aid; HCI). The SSQ disability profiles of each group were compared with those observed in similar studies using the English version and other translations of the SSQ. Standard values, internal consistency, sensitivity, and floor and ceiling effects were investigated, and the missing-response rates to specific questions were calculated. Relationships to speech perception were measured using monosyllabic word scores and the Norwegian Hearing in Noise Test scores.

RESULTS: In the BCI group, the average scores were around 5.0 for the speech and spatial sections and 7.0 for the qualities section (SD ∼2). The average scores of the UCI and HCI groups were about one point lower than those of the BCI group. The SSQ disability profiles were comparable to the profiles in similar studies. The slopes of the linear regression lines measuring the relationships between the SSQ speech and monosyllabic word scores were 0.8 per 10% increase in the monosyllabic word score for the BCI group (explaining 35% of the variation) and 0.4 for the UCI and HCI groups (explaining 22-23% of the variation).

CONCLUSION: The Norwegian version of the SSQ measures hearing disability similar to the original English version, and the internal consistency is good. Differences in the recipients' pre-implantation variables could explain some variations we observed in the SSQ responses, and such predictors should be investigated. Data aggregation will be possible using the SSQ as a routine clinical assessment in global CI populations. Moreover, pre-implantation variables should be systematically registered so that they can be used in mixed-effects models.


ESP Quick Facts

ESP Origins

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.

ESP Support

In 1995, Robbins became the VP/IT of the Fred Hutchinson Cancer Research Center in Seattle, WA. Soon after arriving in Seattle, Robbins secured funding, through the ELSI component of the US Human Genome Project, to create the original ESP.ORG web site, with the formal goal of providing free, world-wide access to the literature of classical genetics.

ESP Rationale

Although the methods of molecular biology can seem almost magical to the uninitiated, the original techniques of classical genetics are readily appreciated by one and all: cross individuals that differ in some inherited trait, collect all of the progeny, score their attributes, and propose mechanisms to explain the patterns of inheritance observed.

ESP Goal

In reading the early works of classical genetics, one is drawn, almost inexorably, into ever more complex models, until molecular explanations begin to seem both necessary and natural. At that point, the tools for understanding genome research are at hand. Assisting readers reach this point was the original goal of The Electronic Scholarly Publishing Project.

ESP Usage

Usage of the site grew rapidly and has remained high. Faculty began to use the site for their assigned readings. Other on-line publishers, ranging from The New York Times to Nature referenced ESP materials in their own publications. Nobel laureates (e.g., Joshua Lederberg) regularly used the site and even wrote to suggest changes and improvements.

ESP Content

When the site began, no journals were making their early content available in digital format. As a result, ESP was obliged to digitize classic literature before it could be made available. For many important papers — such as Mendel's original paper or the first genetic map — ESP had to produce entirely new typeset versions of the works, if they were to be available in a high-quality format.

ESP Help

Early support from the DOE component of the Human Genome Project was critically important for getting the ESP project on a firm foundation. Since that funding ended (nearly 20 years ago), the project has been operated as a purely volunteer effort. Anyone wishing to assist in these efforts should send an email to Robbins.

ESP Plans

With the development of methods for adding typeset side notes to PDF files, the ESP project now plans to add annotated versions of some classical papers to its holdings. We also plan to add new reference and pedagogical material. We have already started providing regularly updated, comprehensive bibliographies to the ESP.ORG site.

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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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Papers in Classical Genetics

The ESP began as an effort to share a handful of key papers from the early days of classical genetics. Now the collection has grown to include hundreds of papers, in full-text format.

Digital Books

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).


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


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 )