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Bibliography on: Brain-Computer Interface

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ESP: PubMed Auto Bibliography 26 May 2024 at 01:40 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: 2024-05-25

Gouret A, Le Bars S, Porssut T, et al (2024)

Advancements in brain-computer interfaces for the rehabilitation of unilateral spatial neglect: a concise review.

Frontiers in neuroscience, 18:1373377.

This short review examines recent advancements in neurotechnologies within the context of managing unilateral spatial neglect (USN), a common condition following stroke. Despite the success of brain-computer interfaces (BCIs) in restoring motor function, there is a notable absence of effective BCI devices for treating cerebral visual impairments, a prevalent consequence of brain lesions that significantly hinders rehabilitation. This review analyzes current non-invasive BCIs and technological solutions dedicated to cognitive rehabilitation, with a focus on visuo-attentional disorders. We emphasize the need for further research into the use of BCIs for managing cognitive impairments and propose a new potential solution for USN rehabilitation, by combining the clinical subtleties of this syndrome with the technological advancements made in the field of neurotechnologies.

RevDate: 2024-05-25

Chen W, Liu X, Wan P, et al (2024)

Anti-artifacts techniques for neural recording front-ends in closed-loop brain-machine interface ICs.

Frontiers in neuroscience, 18:1393206.

In recent years, thanks to the development of integrated circuits, clinical medicine has witnessed significant advancements, enabling more efficient and intelligent treatment approaches. Particularly in the field of neuromedical, the utilization of brain-machine interfaces (BMI) has revolutionized the treatment of neurological diseases such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. The BMI acquires neural signals via recording circuits and analyze them to regulate neural stimulator circuits for effective neurological treatment. However, traditional BMI designs, which are often isolated, have given way to closed-loop brain-machine interfaces (CL-BMI) as a contemporary development trend. CL-BMI offers increased integration and accelerated response speed, marking a significant leap forward in neuromedicine. Nonetheless, this advancement comes with its challenges, notably the stimulation artifacts (SA) problem inherent to the structural characteristics of CL-BMI, which poses significant challenges on the neural recording front-ends (NRFE) site. This paper aims to provide a comprehensive overview of technologies addressing artifacts in the NRFE site within CL-BMI. Topics covered will include: (1) understanding and assessing artifacts; (2) exploring the impact of artifacts on traditional neural recording front-ends; (3) reviewing recent technological advancements aimed at addressing artifact-related issues; (4) summarizing and classifying the aforementioned technologies, along with an analysis of future trends.

RevDate: 2024-05-25

Arulkumaran K, Di Vincenzo M, Dossa RFJ, et al (2024)

A comparison of visual and auditory EEG interfaces for robot multi-stage task control.

Frontiers in robotics and AI, 11:1329270.

Shared autonomy holds promise for assistive robotics, whereby physically-impaired people can direct robots to perform various tasks for them. However, a robot that is capable of many tasks also introduces many choices for the user, such as which object or location should be the target of interaction. In the context of non-invasive brain-computer interfaces for shared autonomy-most commonly electroencephalography-based-the two most common choices are to provide either auditory or visual stimuli to the user-each with their respective pros and cons. Using the oddball paradigm, we designed comparable auditory and visual interfaces to speak/display the choices to the user, and had users complete a multi-stage robotic manipulation task involving location and object selection. Users displayed differing competencies-and preferences-for the different interfaces, highlighting the importance of considering modalities outside of vision when constructing human-robot interfaces.

RevDate: 2024-05-23
CmpDate: 2024-05-23

Yaeger K, J Mocco (2024)

Venous Sinus Stent to Treat Paralysis.

Neurosurgery clinics of North America, 35(3):375-378.

Transvenous treatment of paralysis is a concept less than a decade old. The Stentrode (Synchron, Inc, New York, USA) is a novel electrode on stent device intended to be implanted in the superior sagittal sinus adjacent to the motor cortex. Initial animal studies in sheep demonstrated the safety of the implant as well as its accuracy in detecting neural signals at both short and long term. Early human trials have shown the safety of the device and demonstrated the use of the Stentrode system in facilitating patients with paralysis to carry out daily activities such as texting, email, and personal finance. This is an emerging technology with promise, although certainly more research is required to better understand the capabilities and limitations of the device.

RevDate: 2024-05-23

Kapgate DD (2024)

The Use of Happy Faces as Visual Stimuli Improves the Performance of the Hybrid SSVEP+P300 Brain Computer Interface.

Journal of neuroscience methods pii:S0165-0270(24)00115-8 [Epub ahead of print].

BACKGROUND: This study illustrates a hybrid brain-computer interface (BCI) in which steady-state visual evoked potentials (SSVEP) and event-related potentials (P300) are evoked simultaneously. The goal of this study was to improve the performance of the current hybrid SSVEP+P300 BCI systems by incorporating a happy face into visual stimuli.

NEW METHOD: In this study, happy and sad faces were added to a visual stimulus to induce stronger cortical signals in a hybrid SSVEP+P300 BCI. Additionally, we developed a paradigm in which SSVEP responses were triggered by non-face stimuli, whereas P300 responses were triggered by face stimuli. We tested four paradigms: happy face paradigm (HF), sad face paradigm (SF), happy face and flicker paradigm (HFF), and sad face and flicker paradigm (SFF).

RESULTS AND CONCLUSIONS: The results demonstrated that the HFF paradigm elicited more robust cortical responses, which resulted in enhanced system accuracy and information transfer rate (ITR). The HFF paradigm has a system communication rate of 25.9 bits per second and an average accuracy of 96.1%. Compared with other paradigms, the HFF paradigm is the best choice for BCI applications because it has the highest ITR and maximum level of comfort.

RevDate: 2024-05-23

R V, M Ramasubba Reddy (2024)

Optimizing motor imagery BCI models with hard trials removal and model refinement.

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

Deep learning models have demonstrated remarkable performance in the classification of motor imagery BCI systems. However, these models exhibit sensitivity to challenging trials, often called hard trials, leading to performance degradation. In this paper, we address this issue by proposing two novel methods for identifying and mitigating the impact of hard trials on model performance. The first method leverages model prediction scores to discern hard trials. The second approach employs a quantitative explainable artificial intelligence (XAI) approach, enabling a more transparent and interpretable means of hard trials identification. The identified hard trials are removed from the entire motor imagery training and validation dataset, and the deep learning model is further re-trained using the dataset without hard trials. To evaluate the efficacy of these proposed methods, experiments were conducted on the Open BMI dataset. The results for hold-out analysis show that, the proposed quantitative XAI based hard trial removal method has statistically improved the average classification accuracy of the baseline deep CNN model from 63.77 % to 68.70 %, with p-value = 7.66 -11 for the subject specific MI classification. Additionally, analyzing the scalp map depicting the average relevance scores of correctly classified trials compared to a misclassified trial provides a deeper insight into identifying hard trials. The results indicates that the proposed quantitative based XAI approach outperforms the prediction-score based approach in hard trial identification. .

RevDate: 2024-05-25
CmpDate: 2024-05-23

Kojima S, S Kanoh (2024)

An auditory brain-computer interface based on selective attention to multiple tone streams.

PloS one, 19(5):e0303565.

In this study, we attempted to improve brain-computer interface (BCI) systems by means of auditory stream segregation in which alternately presented tones are perceived as sequences of various different tones (streams). A 3-class BCI using three tone sequences, which were perceived as three different tone streams, was investigated and evaluated. Each presented musical tone was generated by a software synthesizer. Eleven subjects took part in the experiment. Stimuli were presented to each user's right ear. Subjects were requested to attend to one of three streams and to count the number of target stimuli in the attended stream. In addition, 64-channel electroencephalogram (EEG) and two-channel electrooculogram (EOG) signals were recorded from participants with a sampling frequency of 1000 Hz. The measured EEG data were classified based on Riemannian geometry to detect the object of the subject's selective attention. P300 activity was elicited by the target stimuli in the segregated tone streams. In five out of eleven subjects, P300 activity was elicited only by the target stimuli included in the attended stream. In a 10-fold cross validation test, a classification accuracy over 80% for five subjects and over 75% for nine subjects was achieved. For subjects whose accuracy was lower than 75%, either the P300 was also elicited for nonattended streams or the amplitude of P300 was small. It was concluded that the number of selected BCI systems based on auditory stream segregation can be increased to three classes, and these classes can be detected by a single ear without the aid of any visual modality.

RevDate: 2024-05-23

Chen SY, Chang CM, Chiang KJ, et al (2024)

SSVEP-DAN: Cross-Domain Data Alignment for SSVEP-based Brain-Computer Interfaces.

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

Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency is highly dependent on individual training data acquired during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we introduce SSVEP-DAN, the first dedicated neural network model designed to align SSVEP data across different domains, encompassing various sessions, subjects, or devices. Our experimental results demonstrate the ability of SSVEP-DAN to transform existing source SSVEP data into supplementary calibration data. This results in a significant improvement in SSVEP decoding accuracy while reducing the calibration time. We envision SSVEP-DAN playing a crucial role in future applications of high-performance SSVEP-based BCIs. The source code for this work is available at:

RevDate: 2024-05-24

Dichen , Huang H, Guan Z, et al (2024)

An Intersubject Brain-Computer Interface Based on Domain-Adversarial Training of Convolutional Neural Network for Online Attention Decoding.

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

Attention decoding plays a vital role in daily life, where electroencephalography (EEG) has been widely involved. However, training a universally effective model for everyone is impractical due to substantial interindividual variability in EEG signals. To tackle the above challenge, we propose an end-to-end brain-computer interface (BCI) framework, including temporal and spatial one-dimensional (1D) convolutional neural network and domain-adversarial training strategy, namely DA-TSnet. Specifically, DA-TSnet extracts temporal and spatial features of EEG, while it is jointly supervised by task loss and domain loss. During training, DA-TSnet aims to maximize the domain loss while simultaneously minimizing the task loss. We conduct an offline analysis, simulate online experiments on a self-collected dataset of 85 subjects, and real online experiments on 22 subjects. Main results: DA-TSnet achieves a leave-one-subject-out (LOSO) cross-validation (CV) classification accuracy of 89.40% ± 9.96%, outperforming several state-of-the-art attention EEG decoding methods. In simulated online experiments, DA-TSnet achieves an outstanding accuracy of 88.07% ± 11.22%. In real online experiments, it achieves an average accuracy surpassing 86%. Significance: An end-to-end network framework does not rely on elaborate preprocessing and feature extraction steps, which saves time and human workload. Moreover, our framework utilizes domain-adversarial training neural network (DANN) to tackle the challenge posed by the high interindividual variability in EEG signals, which has significant reference value for handling other EEG signal decoding issues. Last, the performance of the DA-TSnet framework in offline and online experiments underscores its potential to facilitate more reliable applications.

RevDate: 2024-05-23

Ge Q, Lock M, Yang X, et al (2024)

Utilizing fMRI to Guide TMS Targets: the Reliability and Sensitivity of fMRI Metrics at 3 T and 1.5 T.

Neuroinformatics [Epub ahead of print].

US Food and Drug Administration (FDA) cleared a Transcranial Magnetic Stimulation (TMS) system with functional Magnetic Resonance Imaging-guided (fMRI) individualized treatment protocol for major depressive disorder, which employs resting state-fMRI (RS-fMRI) functional connectivity (FC) to pinpoint the target individually to increase the accuracy and effeteness of the stimulation. Furthermore, task activation-guided TMS, as well as the use of RS-fMRI local metrics for targeted the specific abnormal brain regions, are considered a precise scheme for TMS targeting. Since 1.5 T MRI is more available in hospitals, systematic evaluation of the test-retest reliability and sensitivity of fMRI metrics on 1.5 T and 3 T MRI may provide reference for the application of fMRI-guided individualized-precise TMS stimulation. Twenty participants underwent three RS-fMRI scans and one scan of finger-tapping task fMRI with self-initiated (SI) and visual-guided (VG) conditions at both 3 T and 1.5 T. Then the location reliability derived by FC (with three seed regions) and peak activation were assessed by intra-individual distance. The test-retest reliability and sensitivity of five RS-fMRI local metrics were evaluated using intra-class correlation and effect size, separately. The intra-individual distance of peak activation location between 1.5 T and 3 T was 15.8 mm and 19 mm for two conditions, respectively. The intra-individual distance for the FC derived targets at 1.5 T was 9.6-31.2 mm, compared to that of 3 T (7.6-31.1 mm). The test-retest reliability and sensitivity of RS-fMRI local metrics showed similar trends on 1.5 T and 3 T. These findings hasten the application of fMRI-guided individualized TMS treatment in clinical practice.

RevDate: 2024-05-25
CmpDate: 2024-05-23

Lu J, Jin Y, Liang S, et al (2024)

Risk factors and their association network for young adults' suicidality: a cross-sectional study.

BMC public health, 24(1):1378.

BACKGROUND: Understanding the intricate influences of risk factors contributing to suicide among young individuals remains a challenge. The current study employed interpretable machine learning and network analysis to unravel critical suicide-associated factors in Chinese university students.

METHODS: A total of 68,071 students were recruited between Sep 2016 and Sep 2020 in China. Students reported their lifetime experiences with suicidal thoughts and behaviors, categorized as suicide ideation (SI), suicide plan (SP), and suicide attempt (SA). We assessed 36 suicide-associated factors including psychopathology, family environment, life events, and stigma. Local interpretations were provided using Shapley additive explanation (SHAP) interaction values, while a mixed graphical model facilitated a global understanding of their interplay.

RESULTS: Local explanations based on SHAP interaction values suggested that psychoticism and depression severity emerged as pivotal factors for SI, while paranoid ideation strongly correlated with SP and SA. In addition, childhood neglect significantly predicted SA. Regarding the mixed graphical model, a hierarchical structure emerged, suggesting that family factors preceded proximal psychopathological factors, with abuse and neglect retaining unique effects. Centrality indices derived from the network highlighted the importance of subjective socioeconomic status and education in connecting various risk factors.

CONCLUSIONS: The proximity of psychopathological factors to suicidality underscores their significance. The global structures of the network suggested that co-occurring factors influence suicidal behavior in a hierarchical manner. Therefore, prospective prevention strategies should take into account the hierarchical structure and unique trajectories of factors.

RevDate: 2024-05-22

Junqueira B, Aristimunha B, Chevallier S, et al (2024)

A systematic evaluation of Euclidean alignment with deep learning for EEG decoding.

Journal of neural engineering [Epub ahead of print].

Electroencephalography (EEG) signals are frequently used for various Brain-Computer Interface (BCI) tasks. While Deep Learning (DL) techniques have shown promising results, they are hindered by the substantial data requirements. By leveraging data from multiple subjects, transfer learning enables more effective training of DL models. A technique that is gaining popularity is Euclidean Alignment (EA) due to its ease of use, low computational complexity, and compatibility with Deep Learning models. However, few studies evaluate its impact on the training performance of shared and individual DL models. In this work, we systematically evaluate the effect of EA combined with DL for decoding BCI signals. We used EA to train shared models with data from multiple subjects and evaluated its transferability to new subjects. Our experimental results show that it improves decoding in the target subject by 4.33% and decreases convergence time by more than 70%. We also trained individual models for each subject to use as a majority-voting ensemble classifier. In this scenario, using EA improved the 3-model ensemble accuracy by 3.7%. However, when compared to the shared model with EA, the ensemble accuracy was 3.62% lower.

RevDate: 2024-05-23

Fukushi T (2024)

East Asian perspective of responsible research and innovation in neurotechnology.

IBRO neuroscience reports, 16:582-597.

After more than half a century of research and development (R&D), Brain-computer interface (BCI)-based Neurotechnology continues to progress as one of the leading technologies of the 2020 s worldwide. Various reports and academic literature in Europe and the United States (U.S.) have outlined the trends in the R&D of neurotechnology and the consideration of ethical issues, and the importance of the formulation of ethical principles, guidance and industrial standards as well as the development of relevant human resources has been discussed. However, limited number studies have focused on neurotechnology R&D, the dissemination of neuroethics related to the academic foundation advancing the discussion on ethical principles, guidance and standards or human resource development in the Asian region. This study fills in this gap in understanding of Eastern Asian (China, Korea and Japan) situation based on the participation in activities to develop ethical principles, guidance, and industrial standards for appropriate use of neurotechnology, in addition to literature survey and clinical registries' search investigation reflecting the trends in neurotechnology R&D as well as its social implication in Asian region. The current study compared the results with the situation in Europa and the U.S. and discussed issues that need to be addressed in the future and discussed the significance and potential of corporate consortium initiatives in Japan and examples of ethics and governance activities in Asian Countries.

RevDate: 2024-05-22

Tang H, Li Y, Liao S, et al (2024)

Multifunctional Conductive Hydrogel Interface for Bioelectronic Recording and Stimulation.

Advanced healthcare materials [Epub ahead of print].

The past few decades have witnessed the rapid advancement and broad applications of flexible bioelectronics, in wearable and implantable electronics, brain-computer interfaces, neural science and technology, clinical diagnosis and treatment, etc. It is noteworthy that soft and elastic conductive hydrogels (CHs), owing to their multiple similarities with biological tissues in terms of mechanics, electronics, water-rich, and biological functions, have successfully bridged the gap between rigid electronics and soft biology. Multifunctional hydrogel bioelectronics, emerging as a new generation of promising material candidates, have authentically established highly compatible and reliable, high-quality bioelectronic interfaces, particularly in bioelectronic recording and stimulation. In this review, we summarize the material basis and design principles involved in constructing hydrogel bioelectronic interfaces, and systematically discuss the fundamental mechanism and unique advantages in bioelectrical interfacing with the biological surface. Furthermore, an overview of the state-of-the-art manufacturing strategies for hydrogel bioelectronic interfaces with enhanced biocompatibility and integration with the biological system is presented. This review finally exemplifies the unprecedented advancement and impetus towards bioelectronic recording and stimulation, especially in implantable and integrated hydrogel bioelectronic systems, and concludes with a perspective expectation for hydrogel bioelectronics in clinical and biomedical applications. This article is protected by copyright. All rights reserved.

RevDate: 2024-05-21
CmpDate: 2024-05-21

Guo B, Mao T, Tao R, et al (2024)

Test-retest reliability and time-of-day variations of perfusion imaging at rest and during a vigilance task.

Cerebral cortex (New York, N.Y. : 1991), 34(5):.

Arterial spin-labeled perfusion and blood oxygenation level-dependent functional MRI are indispensable tools for noninvasive human brain imaging in clinical and cognitive neuroscience, yet concerns persist regarding the reliability and reproducibility of functional MRI findings. The circadian rhythm is known to play a significant role in physiological and psychological responses, leading to variability in brain function at different times of the day. Despite this, test-retest reliability of brain function across different times of the day remains poorly understood. This study examined the test-retest reliability of six repeated cerebral blood flow measurements using arterial spin-labeled perfusion imaging both at resting-state and during the psychomotor vigilance test, as well as task-induced cerebral blood flow changes in a cohort of 38 healthy participants over a full day. The results demonstrated excellent test-retest reliability for absolute cerebral blood flow measurements at rest and during the psychomotor vigilance test throughout the day. However, task-induced cerebral blood flow changes exhibited poor reliability across various brain regions and networks. Furthermore, reliability declined over longer time intervals within the day, particularly during nighttime scans compared to daytime scans. These findings highlight the superior reliability of absolute cerebral blood flow compared to task-induced cerebral blood flow changes and emphasize the importance of controlling time-of-day effects to enhance the reliability and reproducibility of future brain imaging studies.

RevDate: 2024-05-22

Xu M, Zhang Y, Zhang Y, et al (2024)

EEG biomarkers analysis in different cognitive impairment after stroke: an exploration study.

Frontiers in neurology, 15:1358167.

Stroke is a cerebrovascular illness that brings about the demise of brain tissue. It is the third most prevalent cause of mortality worldwide and a significant contributor to physical impairment. Generally, stroke is triggered by blood clots obstructing the brain's blood vessels, or when these vessels rupture. And, the cognitive impairment's evaluation and detection after stroke is crucial research issue and significant project. Thus, the objective of this work is to explore an potential neuroimage tool and find their EEG biomarkers to evaluate and detect four cognitive impairment levels after stroke. In this study, power density spectrum (PSD), functional connectivity map, and one-way ANOVA methods were proposed to analyze the EEG biomarker differences, and the number of patient participants were thirty-two human including eight healthy control, mild, moderate, severe cognitive impairment levels, respectively. Finally, healthy control has significant PSD differences compared to mid, moderate and server cognitive impairment groups. And, the theta and alpha bands of severe cognitive impairment groups have presented consistent superior PSD power at the right frontal cortex, and the theta and beta bands of mild, moderated cognitive impairment (MoCI) groups have shown significant similar superior PSD power tendency at the parietal cortex. The significant gamma PSD power difference has presented at the left-frontal cortex in the mild cognitive impairment (MCI) groups, and severe cognitive impairment (SeCI) group has shown the significant PSD power at the gamma band of parietal cortex. At the point of functional connectivity map, the SeCI group appears to have stronger functional connectivity compared to the other groups. In conclusion, EEG biomarkers can be applied to classify different cognitive impairment groups after stroke. These findings provide a new approach for early detection and diagnosis of cognitive impairment after stroke and also for the development of new treatment options.

RevDate: 2024-05-23
CmpDate: 2024-05-20

Komeiji S, Mitsuhashi T, Iimura Y, et al (2024)

Feasibility of decoding covert speech in ECoG with a Transformer trained on overt speech.

Scientific reports, 14(1):11491.

Several attempts for speech brain-computer interfacing (BCI) have been made to decode phonemes, sub-words, words, or sentences using invasive measurements, such as the electrocorticogram (ECoG), during auditory speech perception, overt speech, or imagined (covert) speech. Decoding sentences from covert speech is a challenging task. Sixteen epilepsy patients with intracranially implanted electrodes participated in this study, and ECoGs were recorded during overt speech and covert speech of eight Japanese sentences, each consisting of three tokens. In particular, Transformer neural network model was applied to decode text sentences from covert speech, which was trained using ECoGs obtained during overt speech. We first examined the proposed Transformer model using the same task for training and testing, and then evaluated the model's performance when trained with overt task for decoding covert speech. The Transformer model trained on covert speech achieved an average token error rate (TER) of 46.6% for decoding covert speech, whereas the model trained on overt speech achieved a TER of 46.3% (p > 0.05 ; d = 0.07) . Therefore, the challenge of collecting training data for covert speech can be addressed using overt speech. The performance of covert speech can improve by employing several overt speeches.

RevDate: 2024-05-20

Ferdi AY, A Ghazli (2024)

Authentication with a one-dimensional CNN model using EEG-based brain-computer interface.

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

Brain-computer interface (BCI) technology uses electroencephalogram (EEG) signals to create a direct interaction between the human body and its surroundings. Motor imagery (MI) classification using EEG signals is an important application that can help a rehabilitated or motor-impaired stroke patient perform certain tasks. Robust classification of these signals is an important step toward making the use of EEG more practical in many applications and less dependent on trained professionals. Deep learning methods have produced impressive results in BCI in recent years, especially with the availability of large electroencephalography (EEG) data sets. Dealing with EEG-MI signals is difficult because noise and other signal sources can interfere with the electrical amplitude of the brain, and its generalization ability is limited, so it is difficult to improve EEG classifiers. To address these issues, this paper presents a methodology based on one-dimensional convolutional neural networks (1-D CNN) for motor imagery (MI) recognition for the right hand, left hand, feet, and sedentary task. The proposed model is a lightweight model with fewer parameters and has an accuracy of 91.75%. Then, in an innovative exploitation of the four output classes, there is an idea that allows people with disabilities who are deprived of security measures, such as entering a secret code, to use the output classification, such as password codes. It is also an idea for a unique authentication system that is more secure and less vulnerable to theft or the like for a healthy person at the same time.

RevDate: 2024-05-19

Zheng J, Wu X, H Xu (2024)

Oxytocinergic Control of a Hypothalamic Social Fear Circuitry.

Neuroscience bulletin [Epub ahead of print].

RevDate: 2024-05-19
CmpDate: 2024-05-19

El Kaim A, Serra M, De Noray H, et al (2024)

Safety and practicality study of using an exoskeleton in acute neurosurgery patients.

Acta neurochirurgica, 166(1):221.

INTRODUCTION: Early mobilization is key in neurologically impaired persons, limiting complications and improving long-term recovery. Self-balanced exoskeletons are used in rehabilitation departments to help patients stand and walk. We report the first case series of exoskeleton use in acute neurosurgery and intensive care patients, evaluating safety, clinical feasibility and patients' satisfaction.

METHODS: We report a retrospective observational study including individuals hospitalized in the neurosurgical intensive care and neurosurgery departments. We included patients with a medical prescription for an exoskeleton session, and who met no contraindication. Patients benefited from standing sessions using a self-balanced exoskeleton (Atalante, Wandercraft, France). Patients and sessions data were collected. Safety, feasibility and adherence were evaluated.

RESULTS: Seventeen patients were scheduled for 70 standing sessions, of which 27 (39%) were completed. They were typically hospitalized for intracranial hemorrhage (74%) and presented with unilateral motor impairments, able to stand but with very insufficient weight shifting to the hemiplegic limb, requiring support (MRC 36.2 ± 3.70, SPB 2.0 ± 1.3, SPD 0.7 ± 0.5). The average duration of standing sessions was 16 ± 9 min. The only side effect was orthostatic hypotension (18.5%), which resolved with returning to seating position. The most frequent reason for not completing a session was understaffing (75%). All patients were satisfied and expressed a desire to repeat it.

CONCLUSIONS: Physiotherapy using the exoskeleton is safe and feasible in the acute neurosurgery setting, although it requires adaptation from the staff to organize the sessions. An efficacy study is ongoing to evaluate the benefits for the patients.

RevDate: 2024-05-21
CmpDate: 2024-05-18

Fukuma R, Majima K, Kawahara Y, et al (2024)

Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition.

Communications biology, 7(1):595.

Dynamic mode (DM) decomposition decomposes spatiotemporal signals into basic oscillatory components (DMs). DMs can improve the accuracy of neural decoding when used with the nonlinear Grassmann kernel, compared to conventional power features. However, such kernel-based machine learning algorithms have three limitations: large computational time preventing real-time application, incompatibility with non-kernel algorithms, and low interpretability. Here, we propose a mapping function corresponding to the Grassmann kernel that explicitly transforms DMs into spatial DM (sDM) features, which can be used in any machine learning algorithm. Using electrocorticographic signals recorded during various movement and visual perception tasks, the sDM features were shown to improve the decoding accuracy and computational time compared to conventional methods. Furthermore, the components of the sDM features informative for decoding showed similar characteristics to the high-γ power of the signals, but with higher trial-to-trial reproducibility. The proposed sDM features enable fast, accurate, and interpretable neural decoding.

RevDate: 2024-05-21
CmpDate: 2024-05-17

Barret N, Guillaumée T, Rimmelé T, et al (2024)

Associations of coping and health-related behaviors with medical students' well-being and performance during objective structured clinical examination.

Scientific reports, 14(1):11298.

Objective structured clinical examination (OSCE) is a valid method to evaluate medical students' competencies. The present cross-sectional study aimed at determining how students' coping and health-related behaviors are associated with their psychological well-being and performance on the day of the OSCE. Fourth-year medical students answered a set of standardized questionnaires assessing their coping (BCI) and health-related behaviors before the examination (sleep PSQI, physical activity GPAQ). Immediately before the OSCE, they reported their level of instant psychological well-being on multi-dimensional visual analogue scales. OSCE performance was assessed by examiners blinded to the study. Associations were explored using multivariable linear regression models. A total of 482 students were included. Instant psychological well-being was positively associated with the level of positive thinking and of physical activity. It was negatively associated with the level of avoidance and of sleep disturbance. Furthermore, performance was negatively associated with the level of avoidance. Positive thinking, good sleep quality, and higher level of physical activity were all associated with improved well-being before the OSCE. Conversely, avoidance coping behaviors seem to be detrimental to both well-being and OSCE performance. The recommendation is to pay special attention to students who engage in avoidance and to consider implementing stress management programs.Clinical trial: The study protocol was registered on NCT05393206, date of registration: 11 June 2022.

RevDate: 2024-05-23

Jiang J, Li C, Chen C, et al (2024)

Tunable and Reversible Adhesive of Liquid Metal Ferrofluid Pillars for Magnetically Actuated Noncontact Transfer Printing.

Advanced materials (Deerfield Beach, Fla.) [Epub ahead of print].

Transfer printing techniques based on tunable and reversible adhesives enable the heterogeneous integration of materials in desired layouts and are essential for developing both existing and envisioned electronic systems. Here, a novel tunable and reversible adhesive of liquid metal ferrofluid pillars for developing an efficient magnetically actuated noncontact transfer printing is reported. The liquid metal ferrofluid pillars offer the appealing advantages of gentle contact force by minimizing the preload effect and exceptional shape adaptability by maximizing the interfacial contact area due to their inherent fluidity, thus enabling a reliable damage-free pickup. Moreover, the liquid metal ferrofluid pillars harness the rapid stiffness increase and shape change with the magnetic field, generating an instantaneous ejection force to achieve a receiver-independent noncontact printing. Demonstrations of the adhesive of liquid metal ferrofluid pillars in transfer printing of diverse objects with different shapes, materials and dimensions onto various substrates illustrate its great potential in deterministic assembly.

RevDate: 2024-05-17

Xu G, Wang Z, Hu H, et al (2024)

Riemannian Locality Preserving Method for Transfer Learning with Applications on Brain-computer Interface.

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

Brain-computer interfaces (BCIs) have been widely focused and extensively studied in recent years for their huge prospect of medical rehabilitation and commercial applications. Transfer learning exploits the information in the source domain and applies in another different but related domain (target domain), and is therefore introduced into the BCIs to figure out the inter-subject variances of electroencephalography (EEG) signals. In this article, a novel transfer learning method is proposed to preserve the Riemannian locality of data structure in both the source and target domains and simultaneously realize the joint distribution adaptation of both domains to enhance the effectiveness of transfer learning. Specifically, a Riemannian graph is first defined and constructed based on the Riemannian distance to represent the Riemannian geometry information. To simultaneously align the marginal and conditional distribution of source and target domains and preserve the Riemannian locality of data structure in both domains, the Riemannian graph is embedded in the joint distribution adaptation (JDA) framework and forms the proposed Riemannian locality preserving-based transfer learning (RLPTL). To validate the effect of the proposed method, it is compared with several existing methods on two open motor imagery datasets, and both multi-source domains (MSD) and single-source domains (SSD) experiments are considered. Experimental results show that the proposed method achieves the highest accuracies in MSD and SSD experiments on three datasets and outperforms eight baseline methods, which demonstrates that the proposed method creates a feasible and efficient way to realize transfer learning.

RevDate: 2024-05-17

Wu Y, Wang L, Yan M, et al (2024)

Poly(3,4-Ethylenedioxythiophene)/Functional Gold Nanoparticle films for Improving the Electrode-Neural Interface.

Advanced healthcare materials [Epub ahead of print].

Implantable neural electrodes are indispensable tools for recording neuron activity, playing a crucial role in neuroscience research. However, traditional neural electrodes suffer from limited electrochemical performance, compromised biocompatibility, and tentative stability, posing great challenges for reliable long-term studies in free-moving animals. In this study, we present a novel approach employing a hybrid film composed of poly(3,4-ethylenedioxythiophene)/functional gold nanoparticles (PEDOT/3-MPA-Au) to improve the electrode-neural interface. The deposited PEDOT/3-MPA-Au demonstrates superior cathodal charge storage capacity, reduced electrochemical impedance, and remarkable electrochemical and mechanical stability. Upon implantation into the cortex of mice for a duration of 12 weeks, the modified electrodes exhibit notably decreased levels of glial fibrillary acidic protein and increased neuronal nuclei immunostaining compared to counterparts utilizing poly(3,4-ethylenedioxythiophene)/poly(styrene sulfonate). Additionally, the PEDOT/3-MPA-Au modified electrodes consistently capture high-quality, stable long-term electrophysiological signals in vivo, enabling continuous recording of target neurons for up to 16 weeks. This innovative modification strategy offers a promising solution for fabricating low-impedance, tissue-friendly, and long-term stable neural interfaces, thereby addressing the shortcomings of conventional neural electrodes. These findings mark a significant advancement towards the development of more reliable and efficacious neural interfaces, with broad implications for both research and clinical applications. This article is protected by copyright. All rights reserved.

RevDate: 2024-05-17
CmpDate: 2024-05-17

Cunlin H, Ye Y, X Nenggang (2024)

Self-supervised motor imagery EEG recognition model based on 1-D MTCNN-LSTM network.

Journal of neural engineering, 21(3):.

Objective.Aiming for the research on the brain-computer interface (BCI), it is crucial to design a MI-EEG recognition model, possessing a high classification accuracy and strong generalization ability, and not relying on a large number of labeled training samples.Approach.In this paper, we propose a self-supervised MI-EEG recognition method based on self-supervised learning with one-dimensional multi-task convolutional neural networks and long short-term memory (1-D MTCNN-LSTM). The model is divided into two stages: signal transform identification stage and pattern recognition stage. In the signal transform recognition phase, the signal transform dataset is recognized by the upstream 1-D MTCNN-LSTM network model. Subsequently, the backbone network from the signal transform identification phase is transferred to the pattern recognition phase. Then, it is fine-tuned using a trace amount of labeled data to finally obtain the motion recognition model.Main results.The upstream stage of this study achieves more than 95% recognition accuracy for EEG signal transforms, up to 100%. For MI-EEG pattern recognition, the model obtained recognition accuracies of 82.04% and 87.14% with F1 scores of 0.7856 and 0.839 on the datasets of BCIC-IV-2b and BCIC-IV-2a.Significance.The improved accuracy proves the superiority of the proposed method. It is prospected to be a method for accurate classification of MI-EEG in the BCI system.

RevDate: 2024-05-16

Yang SH, Huang CJ, JS Huang (2024)

Increasing Robustness of Intracortical Brain-Computer Interfaces for Recording Condition Changes via Data Augmentation.

Computer methods and programs in biomedicine, 251:108208 pii:S0169-2607(24)00204-9 [Epub ahead of print].

BACKGROUND AND OBJECTIVE: Intracortical brain-computer interfaces (iBCIs) aim to help paralyzed individuals restore their motor functions by decoding neural activity into intended movement. However, changes in neural recording conditions hinder the decoding performance of iBCIs, mainly because the neural-to-kinematic mappings shift. Conventional approaches involve either training the neural decoders using large datasets before deploying the iBCI or conducting frequent calibrations during its operation. However, collecting data for extended periods can cause user fatigue, negatively impacting the quality and consistency of neural signals. Furthermore, frequent calibration imposes a substantial computational load.

METHODS: This study proposes a novel approach to increase iBCIs' robustness against changing recording conditions. The approach uses three neural augmentation operators to generate augmented neural activity that mimics common recording conditions. Then, contrastive learning is used to learn latent factors by maximizing the similarity between the augmented neural activities. The learned factors are expected to remain stable despite varying recording conditions and maintain a consistent correlation with the intended movement.

RESULTS: Experimental results demonstrate that the proposed iBCI outperformed the state-of-the-art iBCIs and was robust to changing recording conditions across days for long-term use on one publicly available nonhuman primate dataset. It achieved satisfactory offline decoding performance, even when a large training dataset was unavailable.

CONCLUSIONS: This study paves the way for reducing the need for frequent calibration of iBCIs and collecting a large amount of annotated training data. Potential future works aim to improve offline decoding performance with an ultra-small training dataset and improve the iBCIs' robustness to severely disabled electrodes.

RevDate: 2024-05-16

Serrano-Amenos C, Hu F, Wang PT, et al (2024)

Simulation-Informed Power Budget Estimate of a Fully-Implantable Brain-Computer Interface.

Annals of biomedical engineering [Epub ahead of print].

This study aims to estimate the maximum power consumption that guarantees a thermally safe operation for a titanium-enclosed chest wall unit (CWU) subcutaneously implanted in the pre-pectoral area. This unit is a central piece of an envisioned fully-implantable bi-directional brain-computer interface (BD-BCI). To this end, we created a thermal simulation model using the finite element method implemented in COMSOL. We also performed a sensitivity analysis to ensure that our predictions were robust against the natural variation of physiological and environmental parameters. Based on this analysis, we predict that the CWU can consume between 378 and 538 mW of power without raising the surrounding tissue's temperature above the thermal safety threshold of 2 ∘ C. This power budget should be sufficient to power all of the CWU's basic functionalities, which include training the decoder, online decoding, wireless data transmission, and cortical stimulation. This power budget assessment provides an important specification for the design of a CWU-an integral part of a fully-implantable BD-BCI system.

RevDate: 2024-05-16

Lian J, J Dias Pereira (2024)

Editorial: IoT, UAV, BCI empowered deep learning models in precision agriculture.

Frontiers in plant science, 15:1399753.

RevDate: 2024-05-16

Eck JL, Hernández Hassan L, LS Comita (2024)

Intraspecific plant-soil feedback in four tropical tree species is inconsistent in a field experiment.

American journal of botany [Epub ahead of print].

PREMISE: Soil microbes can influence patterns of diversity in plant communities via plant-soil feedbacks. Intraspecific plant-soil feedbacks occur when plant genotype leads to variations in soil microbial composition, resulting in differences in the performance of seedlings growing near their maternal plants versus seedlings growing near nonmaternal conspecific plants. How consistently such intraspecific plant-soil feedbacks occur in natural plant communities is unclear, especially in variable field conditions.

METHODS: In an in situ experiment with four native tree species on Barro Colorado Island (BCI), Panama, seedlings of each species were transplanted beneath their maternal tree or another conspecific tree in the BCI forest. Mortality and growth were assessed at the end of the wet season (~4 months post-transplant) and at the end of the experiment (~7 months post-transplant).

RESULTS: Differences in seedling performance among field treatments were inconsistent among species and eroded over time. Effects of field environment were detected at the end of the wet season in two of the four species: Virola surinamensis seedlings had higher survival beneath their maternal tree than other conspecific trees, while seedling survival of Ormosia macrocalyx was higher under other conspecific trees. However, these differences were gone by the end of the experiment.

CONCLUSIONS: Our results suggest that intraspecific plant-soil feedbacks may not be consistent in the field for tropical tree species and may have a limited role in determining seedling performance in tropical tree communities. Future studies are needed to elucidate the environmental and genetic factors that determine the incidence and direction of intraspecific plant-soil feedbacks in plant communities.

RevDate: 2024-05-18
CmpDate: 2024-05-16

Yan N, S Hu (2024)

The safety and efficacy of escitalopram and sertraline in post-stroke depression: a randomized controlled trial.

BMC psychiatry, 24(1):365.

OBJECTIVES: This study aims to evaluate the safety and efficacy of escitalopram and sertraline in post-stroke depression (PSD) patients, to provide more reliable therapeutics for cardiovascular and psychiatric clinical practice.

METHODS: We recruited 60 patients (aged 40-89 years old) with an ICD-10 diagnosis of PSD, who were then randomly assigned to two groups and treated with flexible doses of escitalopram (10 to 20 mg/day, n = 30) or sertraline (50 to 200 mg/day, n = 30) for consecutive 8 weeks, respectively. The 24-item Hamilton Depression Rating Scale (HAMD-24), the 14-item Hamilton Anxiety Rating Scale (HAMA-14), the Treatment Emergent Symptom Scale (TESS), the Montreal Cognitive Assessment Scale (MOCA), and the Activity of Daily Living scale (ADL) were used to assess patients before, during, and after treatment for depression, anxiety, adverse effects, cognitive function, and daily living activities. Repeated measures ANOVA, the Mann-Whitney U test, the chi-square test (χ[2]), or Fisher's exact test was employed to assess baseline demographics, response rate, adverse effects rate, and changes in other clinical variables.

RESULTS: Significant reduction in HAMD-24 and HAMA-14 scores was evaluated at baseline, as well as 1, 3, 4, 6, and 8 weeks of drug intervention (p < 0.01). There was a significant group difference in post-treatment HAMD-24 scores (p < 0.05), but no difference was observed in HAMA-14 scores (p > 0.05). Further analysis showed a significant variance in the HAMD-24 scores between the two groups at the end of the first week (p < 0.01). The incidence of adverse effects in both patient groups was mild, but there was a statistically significant difference between the two groups (p < 0.05). The improvement in cognitive function and the recovery of daily living abilities were comparable between both groups (p > 0.05).

CONCLUSION: Escitalopram and sertraline showed comparable efficacy for anxiety symptoms, cognitive function, and daily living abilities in PSD patients. In addition, escitalopram was more appropriate for alleviating depressive symptoms. To validate the conclusion, trials with a larger sample size are in demand in the future. The registration number is ChiCTR1800017373.

RevDate: 2024-05-17

Li S, Yu X, Y Xu (2023)

Breast cancer gene expression signatures: development and clinical significance-a narrative review.

Translational breast cancer research : a journal focusing on translational research in breast cancer, 4:7.

BACKGROUND AND OBJECTIVE: Breast cancer gene expression signatures are developing rapidly and are expected to better understand the intrinsic features of the tumor, and also to optimize the treatment strategy in clinical practice. This review is to summarize the controversy and consensus in clinical practice of gene expression signatures, and to provide our perspective on these issues as well as recommendation for future direction.

METHODS: We reviewed English publications in PubMed related to breast cancer gene expression signatures from 2002 to 2022.

KEY CONTENT AND FINDINGS: Five mature commercial gene expression signatures: Oncotype, MammaPrint, Prosigna/PAM50, EndoPredict and Breast Cancer Index (BCI) are available to provide the prognostic and predictive assessment. Although they could help to evaluate the risk of recurrence and to predict the benefits of certain treatments, their applications remain challenging. Treatment decisions should be determined by a combination of related clinical pathological factors in clinical practice.

CONCLUSIONS: Gene expression signatures could assist in the determination of the adjuvant therapy of early-stage breast cancer. The prospective randomized clinical trials showed that chemotherapy may be exempted in low-risk patients. More sufficient data are expected for the application in radiotherapy, extended endocrine therapy, and neoadjuvant treatment. The treatment cannot be determined by a single factor but by comprehensive assessments of clinicopathological factors, test purpose, and cost-effectiveness. Patients will benefit from personalized treatments with the publication of further evidence.

RevDate: 2024-05-15
CmpDate: 2024-05-15

Hahn RT, Wilkoff BL, Kodali S, et al (2024)

Managing Implanted Cardiac Electronic Devices in Patients With Severe Tricuspid Regurgitation: JACC State-of-the-Art Review.

Journal of the American College of Cardiology, 83(20):2002-2014.

Orthotopic transcatheter tricuspid valve replacement (TTVR) devices have been shown to be highly effective in reducing tricuspid regurgitation (TR), and interest in this therapy is growing with the recent commercial approval of the first orthotopic TTVR. Recent TTVR studies report preexisting cardiac implantable electronic device (CIED) transvalvular leads in ∼35% of patients, with entrapment during valve implantation. Concerns have been raised regarding the safety of entrapping leads and counterbalanced against the risks of transvenous lead extraction (TLE) when indicated. This Heart Valve Collaboratory consensus document attempts to define the patient population with CIED lead-associated or lead-induced TR, describe the risks of lead entrapment during TTVR, delineate the risks and benefits of TLE in this setting, and develop a management algorithm for patients considered for TTVR. An electrophysiologist experienced in CIED management should be part of the multidisciplinary heart team and involved in shared decision making.

RevDate: 2024-05-16

Datta P, Kaur A, Sassi N, et al (2024)

An evaluation of intelligent and immersive digital applications in eliciting cognitive states in humans through the utilization of Emotiv Insight.

MethodsX, 12:102748.

The amalgamation of Virtual Reality (VR) and Artificial Intelligence (AI) results in the development of many promising applications that are helpful for society in many aspects. This research was done to study the effect of immersive and non-immersive applications on user's psychological parameters. In this paper, an intelligent, interactive, and immersive digital application was designed, and the various psychological parameters of users while using the application were analyzed through the brain computer interactive device, Emotiv. The impact of these robust and immersive applications on the emotions of human beings was analyzed. According to the observations, the stress and relaxation levels are getting minimally affected, whereas the engagement levels are high for an immersive application rather than a non-immersive application. Hence, it can be concluded that immersive applications put users "in" the application environment and provide a near-realistic experience by blurring the line between the real and virtual worlds. Deeper immersion results from the increased sensation of presence, which in turn is helpful in increasing motivation and emotional investment.•This paper demonstrates the implementation of the A* algorithm within the Unity 3D Game Engine to develop an intelligent digital application, fostering interactivity and depth.•This paper explores the integration of VR technology to transform the digital application into an immersive and interactive experience, enhancing user engagement and realism.•This paper investigates the utilization of the Emotiv Insight device to analyze cognitive parameters within both non-immersive AI-based and immersive AI & VR-based applications, providing insights into user experiences.

RevDate: 2024-05-16

Livanis E, Voultsos P, Vadikolias K, et al (2024)

Understanding the Ethical Issues of Brain-Computer Interfaces (BCIs): A Blessing or the Beginning of a Dystopian Future?.

Cureus, 16(4):e58243.

In recent years, scientific discoveries in the field of neuroscience combined with developments in the field of artificial intelligence have led to the development of a range of neurotechnologies. Advances in neuroimaging systems, neurostimulators, and brain-computer interfaces (BCIs) are leading to new ways of enhancing, controlling, and "reading" the brain. In addition, although BCIs were developed and used primarily in the medical field, they are now increasingly applied in other fields (entertainment, marketing, education, defense industry). We conducted a literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to provide background information about ethical issues related to the use of BCIs. Among the ethical issues that emerged from the thematic data analysis of the reviewed studies included questions revolving around human dignity, personhood and autonomy, user safety, stigma and discrimination, privacy and security, responsibility, research ethics, and social justice (including access to this technology). This paper attempts to address the various aspects of these concerns. A variety of distinct ethical issues were identified, which, for the most part, were in line with the findings of prior research. However, we identified two nuances, which are related to the empirical research on ethical issues related to BCIs and the impact of BCIs on international relationships. The paper also highlights the need for the cooperation of all stakeholders to ensure the ethical development and use of this technology and concludes with several recommendations. The principles of bioethics provide an initial guiding framework, which, however, should be revised in the current artificial intelligence landscape so as to be responsive to challenges posed by the development and use of BCIs.

RevDate: 2024-05-14
CmpDate: 2024-05-14

Braun JM, Fauth M, Berger M, et al (2024)

A brain machine interface framework for exploring proactive control of smart environments.

Scientific reports, 14(1):11054.

Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces.

RevDate: 2024-05-18
CmpDate: 2024-05-14

Fu Y, Guo T, Zheng J, et al (2024)

Children exhibit superior memory for attended but outdated information compared to adults.

Nature communications, 15(1):4058.

Research on the development of cognitive selectivity predominantly focuses on attentional selection. The present study explores another facet of cognitive selectivity-memory selection-by examining the ability to filter attended yet outdated information in young children and adults. Across five experiments involving 130 children and 130 adults, participants are instructed to use specific information to complete a task, and then unexpectedly asked to report this information in a surprise test. The results consistently demonstrate a developmental reversal-like phenomenon, with children outperforming adults in reporting this kind of attended yet outdated information. Furthermore, we provide evidence against the idea that the results are due to different processing strategies or attentional deployments between adults and children. These results suggest that the ability of memory selection is not fully developed in young children, resulting in their inefficient filtering of attended yet outdated information that is not required for memory retention.

RevDate: 2024-05-14

Cui Q, Liu Z, G Bai (2024)

Friend or foe: The role of stress granule in neurodegenerative disease.

Neuron pii:S0896-6273(24)00286-1 [Epub ahead of print].

Stress granules (SGs) are dynamic membraneless organelles that form in response to cellular stress. SGs are predominantly composed of RNA and RNA-binding proteins that assemble through liquid-liquid phase separation. Although the formation of SGs is considered a transient and protective response to cellular stress, their dysregulation or persistence may contribute to various neurodegenerative diseases. This review aims to provide a comprehensive overview of SG physiology and pathology. It covers the formation, composition, regulation, and functions of SGs, along with their crosstalk with other membrane-bound and membraneless organelles. Furthermore, this review discusses the dual roles of SGs as both friends and foes in neurodegenerative diseases and explores potential therapeutic approaches targeting SGs. The challenges and future perspectives in this field are also highlighted. A more profound comprehension of the intricate relationship between SGs and neurodegenerative diseases could inspire the development of innovative therapeutic interventions against these devastating diseases.

RevDate: 2024-05-14

Qian D, Zeng H, Cheng W, et al (2024)

NeuroDM: Decoding and visualizing human brain activity with EEG-guided diffusion model.

Computer methods and programs in biomedicine, 251:108213 pii:S0169-2607(24)00209-8 [Epub ahead of print].

BACKGROUND AND OBJECTIVE: Brain-Computer Interface (BCI) technology has recently been advancing rapidly, bringing significant hope for improving human health and quality of life. Decoding and visualizing visually evoked electroencephalography (EEG) signals into corresponding images plays a crucial role in the practical application of BCI technology. The recent emergence of diffusion models provides a good modeling basis for this work. However, the existing diffusion models still have great challenges in generating high-quality images from EEG, due to the low signal-to-noise ratio and strong randomness of EEG signals. The purpose of this study is to address the above-mentioned challenges by proposing a framework named NeuroDM that can decode human brain responses to visual stimuli from EEG-recorded brain activity.

METHODS: In NeuroDM, an EEG-Visual-Transformer (EV-Transformer) is used to extract the visual-related features with high classification accuracy from EEG signals, then an EEG-Guided Diffusion Model (EG-DM) is employed to synthesize high-quality images from the EEG visual-related features.

RESULTS: We conducted experiments on two EEG datasets (one is a forty-class dataset, and the other is a four-class dataset). In the task of EEG decoding, we achieved average accuracies of 99.80% and 92.07% on two datasets, respectively. In the task of EEG visualization, the Inception Score of the images generated by NeuroDM reached 15.04 and 8.67, respectively. All the above results outperform existing methods.

CONCLUSIONS: The experimental results on two EEG datasets demonstrate the effectiveness of the NeuroDM framework, achieving state-of-the-art performance in terms of classification accuracy and image quality. Furthermore, our NeuroDM exhibits strong generalization capabilities and the ability to generate diverse images.

RevDate: 2024-05-13

Anonymous (2024)

Brain-machine-interface device translates internal speech into text.

Nature human behaviour [Epub ahead of print].

RevDate: 2024-05-13

Wandelt SK, Bjånes DA, Pejsa K, et al (2024)

Representation of internal speech by single neurons in human supramarginal gyrus.

Nature human behaviour [Epub ahead of print].

Speech brain-machine interfaces (BMIs) translate brain signals into words or audio outputs, enabling communication for people having lost their speech abilities due to diseases or injury. While important advances in vocalized, attempted and mimed speech decoding have been achieved, results for internal speech decoding are sparse and have yet to achieve high functionality. Notably, it is still unclear from which brain areas internal speech can be decoded. Here two participants with tetraplegia with implanted microelectrode arrays located in the supramarginal gyrus (SMG) and primary somatosensory cortex (S1) performed internal and vocalized speech of six words and two pseudowords. In both participants, we found significant neural representation of internal and vocalized speech, at the single neuron and population level in the SMG. From recorded population activity in the SMG, the internally spoken and vocalized words were significantly decodable. In an offline analysis, we achieved average decoding accuracies of 55% and 24% for each participant, respectively (chance level 12.5%), and during an online internal speech BMI task, we averaged 79% and 23% accuracy, respectively. Evidence of shared neural representations between internal speech, word reading and vocalized speech processes was found in participant 1. SMG represented words as well as pseudowords, providing evidence for phonetic encoding. Furthermore, our decoder achieved high classification with multiple internal speech strategies (auditory imagination/visual imagination). Activity in S1 was modulated by vocalized but not internal speech in both participants, suggesting no articulator movements of the vocal tract occurred during internal speech production. This work represents a proof-of-concept for a high-performance internal speech BMI.

RevDate: 2024-05-16
CmpDate: 2024-05-13

Mercier M, Pepi C, Carfi-Pavia G, et al (2024)

The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.

Scientific reports, 14(1):10887.

Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.

RevDate: 2024-05-16
CmpDate: 2024-05-13

Wang J, Yang Q, Liu X, et al (2024)

The basal forebrain to lateral habenula circuitry mediates social behavioral maladaptation.

Nature communications, 15(1):4013.

Elucidating the neural basis of fear allows for more effective treatments for maladaptive fear often observed in psychiatric disorders. Although the basal forebrain (BF) has an essential role in fear learning, its function in fear expression and the underlying neuronal and circuit substrates are much less understood. Here we report that BF glutamatergic neurons are robustly activated by social stimulus following social fear conditioning in male mice. And cell-type-specific inhibition of those excitatory neurons largely reduces social fear expression. At the circuit level, BF glutamatergic neurons make functional contacts with the lateral habenula (LHb) neurons and these connections are potentiated in conditioned mice. Moreover, optogenetic inhibition of BF-LHb glutamatergic pathway significantly reduces social fear responses. These data unravel an important function of the BF in fear expression via its glutamatergic projection onto the LHb, and suggest that selective targeting BF-LHb excitatory circuitry could alleviate maladaptive fear in relevant disorders.

RevDate: 2024-05-13
CmpDate: 2024-05-13

Ramezani Z, André V, S Khizroev (2024)

Modeling the effect of magnetoelectric nanoparticles on neuronal electrical activity: An analog circuit approach.

Biointerphases, 19(3):.

This paper introduces a physical neuron model that incorporates magnetoelectric nanoparticles (MENPs) as an essential electrical circuit component to wirelessly control local neural activity. Availability of such a model is important as MENPs, due to their magnetoelectric effect, can wirelessly and noninvasively modulate neural activity, which, in turn, has implications for both finding cures for neurological diseases and creating a wireless noninvasive high-resolution brain-machine interface. When placed on a neuronal membrane, MENPs act as magnetic-field-controlled finite-size electric dipoles that generate local electric fields across the membrane in response to magnetic fields, thus allowing to controllably activate local ion channels and locally initiate an action potential. Herein, the neuronal electrical characteristic description is based on ion channel activation and inhibition mechanisms. A MENP-based memristive Hodgkin-Huxley circuit model is extracted by combining the Hodgkin-Huxley model and an equivalent circuit model for a single MENP. In this model, each MENP becomes an integral part of the neuron, thus enabling wireless local control of the neuron's electric circuit itself. Furthermore, the model is expanded to include multiple MENPs to describe collective effects in neural systems.

RevDate: 2024-05-13

Xue R, Li X, Deng W, et al (2024)

Shared and distinct electroencephalogram microstate abnormalities across schizophrenia, bipolar disorder, and depression.

Psychological medicine pii:S0033291724001132 [Epub ahead of print].

BACKGROUND: Microstates of an electroencephalogram (EEG) are canonical voltage topographies that remain quasi-stable for 90 ms, serving as the foundational elements of brain dynamics. Different changes in EEG microstates can be observed in psychiatric disorders like schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD). However, the similarities and disparatenesses in whole-brain dynamics on a subsecond timescale among individuals diagnosed with SCZ, BD, and MDD are unclear.

METHODS: This study included 1112 participants (380 individuals diagnosed with SCZ, 330 with BD, 212 with MDD, and 190 demographically matched healthy controls [HCs]). We assembled resting-state EEG data and completed a microstate analysis of all participants using a cross-sectional design.

RESULTS: Our research indicates that SCZ, BD, and MDD exhibit distinct patterns of transition among the four EEG microstate states (A, B, C, and D). The analysis of transition probabilities showed a higher frequency of switching from microstates A to B and from B to A in each patient group compared to the HC group, and less frequent transitions from microstates A to C and from C to A in the SCZ and MDD groups compared to the HC group. And the probability of the microstate switching from C to D and D to C in the SCZ group significantly increased compared to those in the patient and HC groups.

CONCLUSIONS: Our findings provide crucial insights into the abnormalities involved in distributing neural assets and enabling proper transitions between different microstates in patients with major psychiatric disorders.

RevDate: 2024-05-14

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.

Advances in neural information processing systems, 36:42258-42270.

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: 2024-05-13

Poo MM (2024)

China's new ethical guidelines for the use of brain-computer interfaces.

National science review, 11(4):nwae154 pii:nwae154.

RevDate: 2024-05-13
CmpDate: 2024-05-11

Kawaguchi T, Ono K, H Hikawa (2024)

Electroencephalogram-Based Facial Gesture Recognition Using Self-Organizing Map.

Sensors (Basel, Switzerland), 24(9):.

Brain-computer interfaces (BCIs) allow information to be transmitted directly from the human brain to a computer, enhancing the ability of human brain activity to interact with the environment. In particular, BCI-based control systems are highly desirable because they can control equipment used by people with disabilities, such as wheelchairs and prosthetic legs. BCIs make use of electroencephalograms (EEGs) to decode the human brain's status. This paper presents an EEG-based facial gesture recognition method based on a self-organizing map (SOM). The proposed facial gesture recognition uses α, β, and θ power bands of the EEG signals as the features of the gesture. The SOM-Hebb classifier is utilized to classify the feature vectors. We utilized the proposed method to develop an online facial gesture recognition system. The facial gestures were defined by combining facial movements that are easy to detect in EEG signals. The recognition accuracy of the system was examined through experiments. The recognition accuracy of the system ranged from 76.90% to 97.57% depending on the number of gestures recognized. The lowest accuracy (76.90%) occurred when recognizing seven gestures, though this is still quite accurate when compared to other EEG-based recognition systems. The implemented online recognition system was developed using MATLAB, and the system took 5.7 s to complete the recognition flow.

RevDate: 2024-05-10

Kelly AR, DJ Glover (2024)

Information Transmission through Biotic-Abiotic Interfaces to Restore or Enhance Human Function.

ACS applied bio materials [Epub ahead of print].

Advancements in reliable information transfer across biotic-abiotic interfaces have enabled the restoration of lost human function. For example, communication between neuronal cells and electrical devices restores the ability to walk to a tetraplegic patient and vision to patients blinded by retinal disease. These impactful medical achievements are aided by tailored biotic-abiotic interfaces that maximize information transfer fidelity by considering the physical properties of the underlying biological and synthetic components. This Review develops a modular framework to define and describe the engineering of biotic and abiotic components as well as the design of interfaces to facilitate biotic-abiotic information transfer using light or electricity. Delineating the properties of the biotic, interface, and abiotic components that enable communication can serve as a guide for future research in this highly interdisciplinary field. Application of synthetic biology to engineer light-sensitive proteins has facilitated the control of neural signaling and the restoration of rudimentary vision after retinal blindness. Electrophysiological methodologies that use brain-computer interfaces and stimulating implants to bypass spinal column injuries have led to the rehabilitation of limb movement and walking ability. Cellular interfacing methodologies and on-chip learning capability have been made possible by organic transistors that mimic the information processing capacity of neurons. The collaboration of molecular biologists, material scientists, and electrical engineers in the emerging field of biotic-abiotic interfacing will lead to the development of prosthetics capable of responding to thought and experiencing touch sensation via direct integration into the human nervous system. Further interdisciplinary research will improve electrical and optical interfacing technologies for the restoration of vision, offering greater visual acuity and potentially color vision in the near future.

RevDate: 2024-05-10

Zhang Y, ZY Wu (2024)

Chinese patients with adult onset leukodystrophy caused by CST3 variants.

RevDate: 2024-05-13

Hu Z, Zhou Z, H Lyu (2024)

A Power-and-Area-Efficient Channel-Interleaved Neural Signal Processor for Wireless Brain-Computer Interfaces with Unsupervised Spike Sorting.

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

Next generation of wireless brain-computer-interface (BCI) devices require dedicated neural signal processors (NSPs) to extract key neurological information while operating within given power consumption and transmission bandwidth limits. Spike detection and clustering are important signal processing steps in neurological research and clinical applications. Computational-friendly spike detection and feature extraction algorithms are first systematically evaluated in this work. The nonlinear energy operator (NEO) and the first-and-second-derivative (FSDE) together with the 'perturbed' K-mean clustering achieve the highest accuracy performance. An NSP ASIC is implemented in a channel-interleaved architecture and the folding ratio of 16 leads to the minimum power-and-area product. As the result, the NSP consumes 2-μW power consumption and occupies 0.0057 mm2 for each channel in a 65-nm CMOS technology. The proposed system achieves the unsupervised spike classification accuracy of 92% and a data-rate reduction of 98.3%, showing the promise for realizing high-channel-count wireless BCIs.

RevDate: 2024-05-10

Alruwaili R, Alanazi F, Alrashidi A, et al (2024)

Comparative Analysis of Silicone Tube Intubation Versus Probing and Balloon Dilation for Congenital Nasolacrimal Duct Obstruction: A Systematic Review and Meta-Analysis.

The Journal of craniofacial surgery pii:00001665-990000000-01560 [Epub ahead of print].

OBJECTIVE: Congenital nasolacrimal duct obstruction (CNLDO) is a pediatric disorder with a wide range of pathology. If untreated, the condition may end up with serious complications. Multiple treatment options for CNLDO exist throughout the literature, and there is an ongoing debate on the best intervention for each disease subgroup and the best timing of such interventions. This study compares the success and failure rates of silicone tube intubation (STI) against probing and balloon dilation (BD).

METHODS: The authors searched the literature for relevant articles using PubMed, Scopus, web of Science, and Cochrane Library until January 2024. Using RevMan 5.4, the authors compared STI's success and failure rates to probing and BD using risk ratios (RRs) and a random-effect model. In addition, the complication rate of monocanalicular intubation (MCI) versus bicanalicular intubation (BCI) was investigated. The authors used the leave-one-out method to check for influential studies and to resolve heterogeneity.

RESULTS: The screening process resulted in 23 eligible articles for inclusion in the authors' review. Silicone tube intubation had a higher chance of resolving the symptoms of CNLDO than probing (RR = 1.11; 95% CI: 1.04, 1.20; P = 0.004) while having less risk of surgical failure (RR = 0.48; 95% CI: 0.30, 0.76; P = 0.002]. Monocanalicular intubation showed no statistically significant difference when compared with BCI in terms of surgical success and failure; however, MCI had a lower risk of complications (RR = 0.68; 95% CI: 0.48, 0.97; P = 0.04). In addition, STI did not demonstrate any significant difference from BD.

CONCLUSION: There was no significant difference in success/failure between MCI and BCI; monocanalicular had fewer complications. Silicone tube intubation did better in terms of surgical success than probing, especially in children over 12 months, suggesting that it is the preferred intervention for older patients with CNLDO.

RevDate: 2024-05-12
CmpDate: 2024-05-10

Zhao C, Jiang R, Bustillo J, et al (2024)

Cross-cohort replicable resting-state functional connectivity in predicting symptoms and cognition of schizophrenia.

Human brain mapping, 45(7):e26694.

Schizophrenia (SZ) is a debilitating mental illness characterized by adolescence or early adulthood onset of psychosis, positive and negative symptoms, as well as cognitive impairments. Despite a plethora of studies leveraging functional connectivity (FC) from functional magnetic resonance imaging (fMRI) to predict symptoms and cognitive impairments of SZ, the findings have exhibited great heterogeneity. We aimed to identify congruous and replicable connectivity patterns capable of predicting positive and negative symptoms as well as cognitive impairments in SZ. Predictable functional connections (FCs) were identified by employing an individualized prediction model, whose replicability was further evaluated across three independent cohorts (BSNIP, SZ = 174; COBRE, SZ = 100; FBIRN, SZ = 161). Across cohorts, we observed that altered FCs in frontal-temporal-cingulate-thalamic network were replicable in prediction of positive symptoms, while sensorimotor network was predictive of negative symptoms. Temporal-parahippocampal network was consistently identified to be associated with reduced cognitive function. These replicable 23 FCs effectively distinguished SZ from healthy controls (HC) across three cohorts (82.7%, 90.2%, and 86.1%). Furthermore, models built using these replicable FCs showed comparable accuracies to those built using the whole-brain features in predicting symptoms/cognition of SZ across the three cohorts (r = .17-.33, p < .05). Overall, our findings provide new insights into the neural underpinnings of SZ symptoms/cognition and offer potential targets for further research and possible clinical interventions.

RevDate: 2024-05-11

Al-Quraishi MS, Tan WH, Elamvazuthi I, et al (2024)

Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities.

Heliyon, 10(9):e30406.

Electroencephalogram (EEG) signals are critical in interpreting sensorimotor activities for predicting body movements. However, their efficacy in identifying intralimb movements, such as the dorsiflexion and plantar flexion of the foot, remains suboptimal. This study aims to explore whether various EEG signal quantities can effectively recognize intralimb movements to facilitate the development of Brain-Computer Interface (BCI) devices for foot rehabilitation. This research involved twenty-two healthy, right-handed participants. EEG data were collected using 21 electrodes positioned over the motor cortex, while two electromyography (EMG) electrodes recorded the onset of ankle joint movements. The study focused on analyzing slow cortical potential (SCP) and sensorimotor rhythms (SMR) in alpha and beta bands from the EEG. Five key features-fourth-order Autoregressive feature, variance, waveform length, standard deviation, and permutation entropy-were extracted. A modified Recurrent Neural Network (RNN) including Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms was developed for movement recognition. These were compared against conventional machine learning algorithms, including nonlinear Support Vector Machine (SVM) and k Nearest Neighbourhood (kNN) classifiers. The performance of the proposed models was assessed using two data schemes: within-subject and across-subjects. The findings demonstrated that the GRU and LSTM models significantly outperformed traditional machine learning algorithms in recognizing different EEG signal quantities for intralimb movement. The study indicates that deep learning models, particularly GRU and LSTM, hold superior potential over standard machine learning techniques in identifying intralimb movements using EEG signals. Where the accuracies of LSTM for within and across subjects were 98.87 ± 1.80 % and 87.38 ± 0.86 % respectively. Whereas the accuracy of GRU within and across subjects were 99.18 ± 1.28 % and 86.44 ± 0.69 % respectively. This advancement could significantly benefit the development of BCI devices aimed at foot rehabilitation, suggesting a new avenue for enhancing physical therapy outcomes.

RevDate: 2024-05-09

Li Z, Tan X, Li X, et al (2024)

Multiclass motor imagery classification with Riemannian geometry and temporal-spectral selection.

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

Motor imagery (MI) based brain-computer interfaces (BCIs) decode the users' intentions from electroencephalography (EEG) to achieve information control and interaction between the brain and external devices. In this paper, firstly, we apply Riemannian geometry to the covariance matrix extracted by spatial filtering to obtain robust and distinct features. Then, a multiscale temporal-spectral segmentation scheme is developed to enrich the feature dimensionality. In order to determine the optimal feature configurations, we utilize a linear learning-based temporal window and spectral band (TWSB) selection method to evaluate the feature contributions, which efficiently reduces the redundant features and improves the decoding efficiency without excessive loss of accuracy. Finally, support vector machines are used to predict the classification labels based on the selected MI features. To evaluate the performance of our model, we test it on the publicly available BCI Competition IV dataset 2a and 2b. The results show that the method has an average accuracy of 79.1% and 83.1%, which outperforms other existing methods. Using TWSB feature selection instead of selecting all features improves the accuracy by up to about 6%. Moreover, the TWSB selection method can effectively reduce the computational burden. We believe that the framework reveals more interpretable feature information of motor imagery EEG signals, provides neural responses discriminative with high accuracy, and facilitates the performance of real-time MI-BCI.

RevDate: 2024-05-12
CmpDate: 2024-05-09

Saeedinia SA, Jahed-Motlagh MR, Tafakhori A, et al (2024)

Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine.

Scientific reports, 14(1):10667.

The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.

RevDate: 2024-05-09

Aissa NEHSB, Korichi A, Lakas A, et al (2024)

Assessing robustness to adversarial attacks in attention-based networks: Case of EEG-based motor imagery classification.

SLAS technology pii:S2472-6303(24)00024-4 [Epub ahead of print].

The classification of motor imagery (MI) using Electroencephalography (EEG) plays a pivotal role in facilitating communication for individuals with physical limitations through Brain-Computer Interface (BCI) systems. Recent strides in Attention-Based Networks (ATN) have showcased remarkable performance in EEG signal classification, presenting a promising alternative to conventional Convolutional Neural Networks (CNNs). However, while CNNs have been extensively analyzed for their resilience against adversarial attacks, the susceptibility of ATNs in comparable scenarios remains largely unexplored. This paper aims to fill this gap by investigating the robustness of ATNs in adversarial contexts. We propose a high-performing attention-based deep learning model specifically designed for classifying Motor Imagery (MI) brain signals extracted from EEG data. Subsequently, we conduct a thorough series of experiments to assess various attack strategies targeting ATNs employed in EEG-based BCI tasks. Our analysis utilizes the widely recognized BCI Competition 2a dataset to demonstrate the effectiveness of attention mechanisms in BCI endeavors. Despite achieving commendable classification results in terms of accuracy (87.15%) and kappa score (0.8287), our findings reveal the vulnerability of attention-based models to adversarial manipulations (accuracy: 9.07%, kappa score: -0.21), highlighting the imperative for bolstering the robustness of attention architectures for EEG classification tasks.

RevDate: 2024-05-15
CmpDate: 2024-05-15

Lee WH, Karpowicz BM, Pandarinath C, et al (2024)

Identifying Distinct Neural Features between the Initial and Corrective Phases of Precise Reaching Using AutoLFADS.

The Journal of neuroscience : the official journal of the Society for Neuroscience, 44(20): pii:JNEUROSCI.1224-23.2024.

Many initial movements require subsequent corrective movements, but how the motor cortex transitions to make corrections and how similar the encoding is to initial movements is unclear. In our study, we explored how the brain's motor cortex signals both initial and corrective movements during a precision reaching task. We recorded a large population of neurons from two male rhesus macaques across multiple sessions to examine the neural firing rates during not only initial movements but also subsequent corrective movements. AutoLFADS, an autoencoder-based deep-learning model, was applied to provide a clearer picture of neurons' activity on individual corrective movements across sessions. Decoding of reach velocity generalized poorly from initial to corrective submovements. Unlike initial movements, it was challenging to predict the velocity of corrective movements using traditional linear methods in a single, global neural space. We identified several locations in the neural space where corrective submovements originated after the initial reaches, signifying firing rates different than the baseline before initial movements. To improve corrective movement decoding, we demonstrate that a state-dependent decoder incorporating the population firing rates at the initiation of correction improved performance, highlighting the diverse neural features of corrective movements. In summary, we show neural differences between initial and corrective submovements and how the neural activity encodes specific combinations of velocity and position. These findings are inconsistent with assumptions that neural correlations with kinematic features are global and independent, emphasizing that traditional methods often fall short in describing these diverse neural processes for online corrective movements.

RevDate: 2024-05-09

Lamba K, S Rani (2024)

A Novel Approach of Brain-Computer Interfacing (BCI) and Grad-CAM Based Explainable Artificial Intelligence: Use Case Scenario for Smart Healthcare.

Journal of neuroscience methods pii:S0165-0270(24)00104-3 [Epub ahead of print].

BACKGROUND: In order to push the frontiers of brain-computer interface (BCI) and neuron-electronics, this research presents a novel framework that combines cutting-edge technologies for improved brain-related diagnostics in smart healthcare. This research offers a ground-breaking application of transparent strategies to BCI, promoting openness and confidence in brain-computer interactions and taking inspiration from Grad-CAM (Gradient-weighted Class Activation Mapping) based Explainable Artificial Intelligence (XAI) methodology. The landscape of healthcare diagnostics is about to be redefined by the integration of various technologies, especially when it comes to illnesses related to the brain.

NEW METHOD: A novel approach has been proposed in this study comprising of Xception architecture which is trained on imagenet database following transfer learning process for extraction of significant features from magnetic resonance imaging dataset acquired from publicly available distinct sources as an input and linear support vector machine has been used for distinguishing distinct classes.Afterwards, gradient-weighted class activation mapping has been deployed as the foundation for explainable artificial intelligence (XAI) for generating informative heatmaps, representing spatial localization of features which were focused to achieve model's predictions.

RESULTS: Thus, the proposed model not only provides accurate outcomes but also provides transparency for the predictions generated by the Xception network to diagnose presence of abnormal tissues and avoids overfitting issues. Hyperparameters along with performance-metrics are also obtained while validating the proposed network on unseen brain MRI scans to ensure effectiveness of the proposed network.

The integration of Grad-CAM based explainable artificial intelligence with deep neural network namely Xception offers a significant impact in diagnosing brain tumor disease while highlighting the specific regions of input brain MRI images responsible for making predictions. In this study, the proposed network results in 98.92% accuracy, 98.15% precision, 99.09% sensitivity, 98.18% specificity and 98.91% dice-coefficient while identifying presence of abnormal tissues in the brain. Thus, Xception model trained on distinct dataset following transfer learning process offers remarkable diagnostic accuracy and linear support vector act as a classifier to provide efficient classification among distinct classes. In addition, the deployed explainable artificial intelligence approach helps in revealing the reasoning behind predictions made by deep neural network having black-box nature and provides a clear perspective to assist medical experts in achieving trustworthiness and transparency while diagnosing brain tumor disease in the smart healthcare.

RevDate: 2024-05-09

Gong M, Pan C, Pan R, et al (2024)

Distinct patterns of monocular advantage for facial emotions in social anxiety.

Journal of anxiety disorders, 104:102871 pii:S0887-6185(24)00047-1 [Epub ahead of print].

Individuals with social anxiety often exhibit atypical processing of facial expressions. Previous research in social anxiety has primarily emphasized cognitive bias associated with face processing and the corresponding abnormalities in cortico-limbic circuitry, yet whether social anxiety influences early perceptual processing of emotional faces remains largely unknown. We used a psychophysical method to investigate the monocular advantage for face perception (i.e., face stimuli are better recognized when presented to the same eye compared to different eyes), an effect that is indicative of early, subcortical processing of face stimuli. We compared the monocular advantage for different emotional expressions (neutral, angry and sad) in three groups (N = 24 per group): individuals clinically diagnosed with social anxiety disorder (SAD), individuals with high social anxiety in subclinical populations (SSA), and a healthy control (HC) group of individuals matched for age and gender. Compared to SSA and HC groups, we found that individuals with SAD exhibited a greater monocular advantage when processing neutral and sad faces. While the magnitudes of monocular advantages were similar across three groups when processing angry faces, individuals with SAD performed better in this condition when the faces were presented to different eye. The former findings suggest that social anxiety leads to an enhanced role of subcortical structures in processing nonthreatening expressions. The latter findings, on the other hand, likely reflect an enhanced cortical processing of threatening expressions in SAD group. These distinct patterns of monocular advantage indicate that social anxiety altered representation of emotional faces at various stages of information processing, starting at an early stage of the visual system.

RevDate: 2024-05-09

Bi J, Gao Y, Peng Z, et al (2024)

Classification of motor imagery using chaotic entropy based on sub-band EEG source localization.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Electroencephalography (EEG) has been widely used in motor imagery (MI) research by virtue of its high temporal resolution and low cost, but its low spatial resolution is still a major criticism. The EEG source localization (ESL) algorithm effectively improves the spatial resolution of the signal by inverting the scalp EEG to extrapolate the cortical source signal, thus enhancing the classification accuracy.

APPROACH: To address the problem of poor spatial resolution of EEG signals, this paper proposed a sub-band source chaotic entropy (SSCE) feature extraction method based on sub-band ESL. Firstly, the preprocessed EEG signals were filtered into 8 sub-bands. Each sub-band signal was source localized respectively to reveal the activation patterns of specific frequency bands of the EEG signals and the activities of specific brain regions in the MI task. Then, Approximate Entropy (ApEn), Fuzzy Entropy (FE) and Permutation Entropy (PE) were extracted from the source signal as features to quantify the complexity and randomness of the signal. Finally, the classification of different MI tasks was achieved using Support Vector Machine (SVM).

MAIN RESULT: The proposed method was validated on two MI public datasets (BCI competition III IVa, BCI competition IV 2a) and the results showed that the classification accuracies were higher than the existing methods.

SIGNIFICANCE: The spatial resolution of the signal was improved by sub-band EEG localization in the paper, which provided a new idea for EEG MI research. .

RevDate: 2024-05-10

Parikh PM, A Venniyoor (2024)

Neuralink and Brain-Computer Interface-Exciting Times for Artificial Intelligence.

South Asian journal of cancer, 13(1):63-65.

Purvish Mahendra ParikhBrain-computer interfaces are becoming a tangible reality, capable of significantly aiding patients in real-world scenarios. The recent approval by the U.S. Food and Drug Administration for clinical human trials of Neuralink marks a monumental stride, comparable to Mr. Armstrong's moonwalk. Numerous other companies are also pioneering innovative solutions in this domain. Presently, over 150,000 patients in the United States possess brain implants. As technology advances, it holds the potential to alleviate various conditions, notably motor paralysis, cerebral palsy, and involuntary movements.

RevDate: 2024-05-08

Ajrawi SA, Rao R, M Sarkar (2024)

A Hierarchical Recursive Feature Elimination Algorithm to develop Brain Computer Interface Application of User Behavior for Statistical reasoning and Decision making.

Journal of neuroscience methods pii:S0165-0270(24)00106-7 [Epub ahead of print].

BACKGROUND: With the aid of a brain computer interface (BCI), users can communicate and receive signals wirelessly or over wired connections to operate smart devices. A BCI classifier's architecture is quite difficult since numerous elements should be combined. These elements are made up of brain signals, which also include high levels of weak sounds that could provide reliable participant recordings of daily activities. We must use computer vision techniques to create a model in order to control those information. The high dimension and volume of signals present the classification classifier with its primary obstacles.

NEW METHOD: Due to this, we extracted and classified the brain activity in this study, and we also presented a novel hierarchical recursive feature elimination method that we refer to as HRFE embracing noisy additions. HRFE makes a variety of categorization suggestions to eliminate bias in classifying BCI systems of different types. We put the HRFE to the test on two BCI signal data sets-specifically, dataset I and BCI contests III-using shallow and deep convolution network classification techniques. Just a grid of assets is used to create electrocorticography (ECoG) signals on the contralateral (right) motor cortex, and these signals are recorded in the BCI contests III database.

RESULTS: Using ECoG signals, we choose the top 20 features that have the biggest effects on distortion and classification selection.

The simulation findings show that HRFE has a significant computer vision enhancement when compared to comparable feature selection methods in the literature, particularly for ECoG signal, which achieves about 93% reliability.

RevDate: 2024-05-08

Wimpff M, Gizzi L, Zerfowski J, et al (2024)

EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms.

Journal of neural engineering [Epub ahead of print].

Objective The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding. Channel attention mechanisms can be seen as a powerful evolution of spatial filters traditionally used for motor imagery decoding. This study systematically compares such mechanisms by integrating them into a lightweight architecture framework to evaluate their impact. Approach We carefully construct a straightforward and lightweight baseline architecture designed to seamlessly integrate different channel attention mechanisms. This approach is contrary to previous works which only investigate one attention mechanism and usually build a very complex, sometimes nested architecture. Our framework allows us to evaluate and compare the impact of different attention mechanisms under the same circumstances. The easy integration of different channel attention mechanisms as well as the low computational complexity enables us to conduct a wide range of experiments on four datasets to thoroughly assess the effectiveness of the baseline model and the attention mechanisms. Results Our experiments demonstrate the strength and generalizability of our architecture framework as well as how channel attention mechanisms can improve the performance while maintaining the small memory footprint and low computational complexity of our baseline architecture. Significance Our architecture emphasizes simplicity, offering easy integration of channel attention mechanisms, while maintaining a high degree of generalizability across datasets, making it a versatile and efficient solution for EEG motor imagery decoding within brain-computer interfaces.

RevDate: 2024-05-08

Xiangcun W, Zhang J, X Wu (2024)

A feature enhanced EEG compression model using asymmetric encoding-decoding network.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Recently, the demand for wearable devices using electroencephalography (EEG) has increased rapidly in many fields. Due to its volume and computation constraints, wearable devices usually compress and transmit EEG to external devices for analysis. However, current EEG compression algorithms are not tailor-made for wearable devices with limited computing and storage. Firstly, the huge amount of parameters makes it difficult to apply in wearable devices; secondly, it's tricky to learn EEG signals' distribution law due to the low signal-to-noise ratio, which leads to excessive reconstruction error and suboptimal compression performance.

APPROACH: Here, a feature enhanced asymmetric encoding-decoding network is proposed. EEG is encoded with a lightweight model, and subsequently decoded with a multi-level feature fusion network by extracting the encoded features deeply and reconstructing the signal through a two-branch structure.

MAIN RESULTS: On public EEG datasets, motor imagery and event-related potentials, experimental results show that the proposed method has achieved the state of the art compression performance. In addition, the neural representation analysis and the classification performance of the reconstructed EEG signals also show that our method tends to retain more task-related information as the compression ratio increases and retains reliable discriminative information after EEG compression.

SIGNIFICANCE: This paper tailors an asymmetric EEG compression method for wearable devices that achieves state-of-the-art compression performance in a lightweight manner, paving the way for the application of EEG-based wearable devices.

RevDate: 2024-05-09

Ghorbani H, AfzalAghai M, Soltani S, et al (2023)

Translation, Linguistic Validation, and Cultural Adaptation of the Bladder Cancer Index (BCI) Questionnaire Into the Persian (Farsi) Language and Comparing it With WHO Quality of Life Questionnaire: An Observational Study.

Journal of family & reproductive health, 17(3):128-135.

OBJECTIVE: Whether ileal conduit diversion (ICD) or orthotopic neobladder (ONB) urinary diversion provides better quality of life (QoL) is still under debate. The Bladder Cancer Index (BCI) is a specific tool for bladder cancer (BCa) patients, providing reliable results in previous studies. A validated Farsi version of the BCI concerning cultural aspects could help Farsi-speaking clinicians gain more reliable feedback on QoL following urinary diversion.

MATERIALS AND METHODS: Based on WHO suggestions, we translated the BCI questionnaire into the Persian language. Then, we performed a cross-sectional study on BCa patients who underwent ICD or ONB urinary diversion. We compared their QoL via BCI and WHO questionnaires. Chi-square and independent t-tests were used where appropriate.

RESULTS: The content validity ratio and the content validity indexes were 1 and 0.8-1.0, respectively. Of 57 participants, six patients (10.5%) were women. The ICD was performed for 38 (66.7%) and ONB diversion for 19 (33.3) participants. The mean age of ICD and ONB was 68.71 ± 7.40 and 64.28 ± 8.34 years, respectively (p-value: 0.055). In all sub-domains of BCI, except bowel habits, the mean scores were higher in the ICD group. A significant difference between ICD and ONB groups was found regarding urinary function (p-value<0.001). There was no significant difference between ICD and ONB groups in none of the domains of the WHO questionnaire.

CONCLUSION: The QoL of ICD and ONB patients did not differ significantly. Even ICD may be superior in ritual purification, while the psychological status of ONB patients was better.

RevDate: 2024-05-07

Webster P (2024)

The future of brain-computer interfaces in medicine.

RevDate: 2024-05-07

Wang Z, Li S, Luo J, et al (2024)

Channel reflection: Knowledge-driven data augmentation for EEG-based brain-computer interfaces.

Neural networks : the official journal of the International Neural Network Society, 176:106351 pii:S0893-6080(24)00275-2 [Epub ahead of print].

A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase user-friendliness, usually a small amount of user-specific EEG data are used for calibration, which may not be enough to develop a pure data-driven decoding model. To cope with this typical calibration data shortage challenge in EEG-based BCIs, this paper proposes a parameter-free channel reflection (CR) data augmentation approach that incorporates prior knowledge on the channel distributions of different BCI paradigms in data augmentation. Experiments on eight public EEG datasets across four different BCI paradigms (motor imagery, steady-state visual evoked potential, P300, and seizure classifications) using different decoding algorithms demonstrated that: (1) CR is effective, i.e., it can noticeably improve the classification accuracy; (2) CR is robust, i.e., it consistently outperforms existing data augmentation approaches in the literature; and, (3) CR is flexible, i.e., it can be combined with other data augmentation approaches to further improve the performance. We suggest that data augmentation approaches like CR should be an essential step in EEG-based BCIs. Our code is available online.

RevDate: 2024-05-07

Jin J, Xu R, Daly I, et al (2024)

MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces.

IEEE transactions on cybernetics, PP: [Epub ahead of print].

Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. Deep learning methods have been increasingly adopted for ERP-based brain-computer interfaces (BCIs) due to their excellent feature representation abilities, which allow for deep analysis of oscillatory activity within the brain. Features with higher spatiotemporal frequencies usually represent detailed and localized information, while features with lower spatiotemporal frequencies usually represent global structures. Mining EEG features from multiple spatiotemporal frequencies is conducive to obtaining more discriminative information. A multiscale feature fusion octave convolution neural network (MOCNN) is proposed in this article. MOCNN divides the ERP signals into high-, medium-and low-frequency components corresponding to different resolutions and processes them in different branches. By adding mid-and low-frequency components, the feature information used by MOCNN can be enriched, and the required amount of calculations can be reduced. After successive feature mapping using temporal and spatial convolutions, MOCNN realizes interactive learning among different components through the exchange of feature information among branches. Classification is accomplished by feeding the fused deep spatiotemporal features from various components into a fully connected layer. The results, obtained on two public datasets and a self-collected ERP dataset, show that MOCNN can achieve state-of-the-art ERP classification performance. In this study, the generalized concept of octave convolution is introduced into the field of ERP-BCI research, which allows effective spatiotemporal features to be extracted from multiscale networks through branch width optimization and information interaction at various scales.

RevDate: 2024-05-07

Cao HL, Meng YJ, Wei W, et al (2024)

Altered individual gray matter structural covariance networks in early abstinence patients with alcohol dependence.

Brain imaging and behavior [Epub ahead of print].

While alterations in cortical thickness have been widely observed in individuals with alcohol dependence, knowledge about cortical thickness-based structural covariance networks is limited. This study aimed to explore the topological disorganization of structural covariance networks based on cortical thickness at the single-subject level among patients with alcohol dependence. Structural imaging data were obtained from 61 patients with alcohol dependence during early abstinence and 59 healthy controls. The single-subject structural covariance networks were constructed based on cortical thickness data from 68 brain regions and were analyzed using graph theory. The relationships between network architecture and clinical characteristics were further investigated using partial correlation analysis. In the structural covariance networks, both patients with alcohol dependence and healthy controls displayed small-world topology. However, compared to controls, alcohol-dependent individuals exhibited significantly altered global network properties characterized by greater normalized shortest path length, greater shortest path length, and lower global efficiency. Patients exhibited lower degree centrality and nodal efficiency, primarily in the right precuneus. Additionally, scores on the Alcohol Use Disorder Identification Test were negatively correlated with the degree centrality and nodal efficiency of the left middle temporal gyrus. The results of this correlation analysis did not survive after multiple comparisons in the exploratory analysis. Our findings may reveal alterations in the topological organization of gray matter networks in alcoholism patients, which may contribute to understanding the mechanisms of alcohol addiction from a network perspective.

RevDate: 2024-05-07

Menéndez JA, Hennig JA, Golub MD, et al (2024)

A theory of brain-computer interface learning via low-dimensional control.

bioRxiv : the preprint server for biology pii:2024.04.18.589952.

A remarkable demonstration of the flexibility of mammalian motor systems is primates' ability to learn to control brain-computer interfaces (BCIs). This constitutes a completely novel motor behavior, yet primates are capable of learning to control BCIs under a wide range of conditions. BCIs with carefully calibrated decoders, for example, can be learned with only minutes to hours of practice. With a few weeks of practice, even BCIs with randomly constructed decoders can be learned. What are the biological substrates of this learning process? Here, we develop a theory based on a re-aiming strategy, whereby learning operates within a low-dimensional subspace of task-relevant inputs driving the local population of recorded neurons. Through comprehensive numerical and formal analysis, we demonstrate that this theory can provide a unifying explanation for disparate phenomena previously reported in three different BCI learning tasks, and we derive a novel experimental prediction that we verify with previously published data. By explicitly modeling the underlying neural circuitry, the theory reveals an interpretation of these phenomena in terms of biological constraints on neural activity.

RevDate: 2024-05-07

Shah NP, Willsey MS, Hahn N, et al (2024)

A flexible intracortical brain-computer interface for typing using finger movements.

bioRxiv : the preprint server for biology pii:2024.04.22.590630.

Keyboard typing with finger movements is a versatile digital interface for users with diverse skills, needs, and preferences. Currently, such an interface does not exist for people with paralysis. We developed an intracortical brain-computer interface (BCI) for typing with attempted flexion/extension movements of three finger groups on the right hand, or both hands, and demonstrated its flexibility in two dominant typing paradigms. The first paradigm is "point-and-click" typing, where a BCI user selects one key at a time using continuous real-time control, allowing selection of arbitrary sequences of symbols. During cued character selection with this paradigm, a human research participant with paralysis achieved 30-40 selections per minute with nearly 90% accuracy. The second paradigm is "keystroke" typing, where the BCI user selects each character by a discrete movement without real-time feedback, often giving a faster speed for natural language sentences. With 90 cued characters per minute, decoding attempted finger movements and correcting errors using a language model resulted in more than 90% accuracy. Notably, both paradigms matched the state-of-the-art for BCI performance and enabled further flexibility by the simultaneous selection of multiple characters as well as efficient decoder estimation across paradigms. Overall, the high-performance interface is a step towards the wider accessibility of BCI technology by addressing unmet user needs for flexibility.

RevDate: 2024-05-07

Downey JE, Schone HR, Foldes ST, et al (2024)

A roadmap for implanting microelectrode arrays to evoke tactile sensations through intracortical microstimulation.

medRxiv : the preprint server for health sciences pii:2024.04.26.24306239.

Intracortical microstimulation (ICMS) is a method for restoring sensation to people with paralysis as part of a bidirectional brain-computer interface to restore upper limb function. Evoking tactile sensations of the hand through ICMS requires precise targeting of implanted electrodes. Here we describe the presurgical imaging procedures used to generate functional maps of the hand area of the somatosensory cortex and subsequent planning that guided the implantation of intracortical microelectrode arrays. In five participants with cervical spinal cord injury, across two study locations, this procedure successfully enabled ICMS-evoked sensations localized to at least the first four digits of the hand. The imaging and planning procedures developed through this clinical trial provide a roadmap for other brain-computer interface studies to ensure successful placement of stimulation electrodes.

RevDate: 2024-05-07

Vakilipour P, S Fekrvand (2024)

Brain-to-brain interface technology: A brief history, current state, and future goals.

International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience [Epub ahead of print].

A brain-to-brain interface (BBI), defined as a combination of neuroimaging and neurostimulation methods to extract and deliver information between brains directly without the need for the peripheral nervous system, is a budding communication technique. A BBI system is made up of two parts known as the brain-computer interface part, which reads a sender's brain activity and digitalizes it, and the computer-brain interface part, which writes the delivered brain activity to a receiving brain. As with other technologies, BBI systems have gone through an evolutionary process since they first appeared. The BBI systems have been employed for numerous purposes, including rehabilitation for post-stroke patients, communicating with patients suffering from amyotrophic lateral sclerosis, locked-in syndrome and speech problems following stroke. Also, it has been proposed that a BBI system could play an important role on future battlefields. This technology was not only employed for communicating between two human brains but also for making a direct communication path among different species through which motor or sensory commands could be sent and received. However, the application of BBI systems has provoked significant challenges to human rights principles due to their ability to access and manipulate human brain information. In this study, we aimed to review the brain-computer interface and computer-brain interface technologies as components of BBI systems, the development of BBI systems, applications of this technology, arising ethical issues and expectations for future use.

RevDate: 2024-05-09
CmpDate: 2024-05-07

Zhou H, Gong L, Su C, et al (2024)

White matter integrity of right frontostriatal circuit predicts internet addiction severity among internet gamers.

Addiction biology, 29(5):e13399.

Excessive use of the internet, which is a typical scenario of self-control failure, could lead to potential consequences such as anxiety, depression, and diminished academic performance. However, the underlying neuropsychological mechanisms remain poorly understood. This study aims to investigate the structural basis of self-control and internet addiction. In a cohort of 96 internet gamers, we examined the relationships among grey matter volume and white matter integrity within the frontostriatal circuits and internet addiction severity, as well as self-control measures. The results showed a significant and negative correlation between dACC grey matter volume and internet addiction severity (p < 0.001), but not with self-control. Subsequent tractography from the dACC to the bilateral ventral striatum (VS) was conducted. The fractional anisotropy (FA) and radial diffusivity of dACC-right VS pathway was negatively (p = 0.011) and positively (p = 0.020) correlated with internet addiction severity, respectively, and the FA was also positively correlated with self-control (p = 0.036). These associations were not observed for the dACC-left VS pathway. Further mediation analysis demonstrated a significant complete mediation effect of self-control on the relationship between FA of the dACC-right VS pathway and internet addiction severity. Our findings suggest that the dACC-right VS pathway is a critical neural substrate for both internet addiction and self-control. Deficits in this pathway may lead to impaired self-regulation over internet usage, exacerbating the severity of internet addiction.

RevDate: 2024-05-06

Chen S, YJ Liu (2024)

Microglia Suppresses Breast Cancer Brain Metastasis via a Pro-inflammatory Response.

Neuroscience bulletin [Epub ahead of print].

RevDate: 2024-05-07

Bridges NR, Stickle M, KA Moxon (2024)

Transitioning from global to local computational strategies during brain-machine interface learning.

Frontiers in neuroscience, 18:1371107.

When learning to use a brain-machine interface (BMI), the brain modulates neuronal activity patterns, exploring and exploiting the state space defined by their neural manifold. Neurons directly involved in BMI control (i.e., direct neurons) can display marked changes in their firing patterns during BMI learning. However, the extent of firing pattern changes in neurons not directly involved in BMI control (i.e., indirect neurons) remains unclear. To clarify this issue, we localized direct and indirect neurons to separate hemispheres in a task designed to bilaterally engage these hemispheres while animals learned to control the position of a platform with their neural signals. Animals that learned to control the platform and improve their performance in the task shifted from a global strategy, where both direct and indirect neurons modified their firing patterns, to a local strategy, where only direct neurons modified their firing rate, as animals became expert in the task. Animals that did not learn the BMI task did not shift from utilizing a global to a local strategy. These results provide important insights into what differentiates successful and unsuccessful BMI learning and the computational mechanisms adopted by the neurons.

RevDate: 2024-05-07

Li X, Tan Y, Song J, et al (2024)

Combined intravenous and intra-arterial thrombolysis in hyperacute cerebral ischemia without significant corresponding vascular occlusion/stenosis: A Preliminary investigation.

Heliyon, 10(9):e29998.

OBJECTIVE: In this study, we assessed the efficacy and safety of various thrombolytic treatment protocols in patients with hyperacute cerebral infarction.

METHODS: Patients diagnosed with acute ischemic stroke within 6 h of symptom onset and with brain computer tomography angiography confirming the absence of major vessel stenosis or occlusion were eligible for this study. The enrolled patients were subsequently randomized into two groups: all the groups received the standard intravenous thrombolysis treatment with rt-PA (0.9 mg/kg), and the experimental group underwent sequential intra-arterial thrombolysis treatment with alteplase (0.3 mg/kg, with a maximum dose of 22 mg), administered directly into the target vessel via a microcatheter. Both groups were closely monitored for changes in their National Institutes of Health Stroke Scale (NIHSS) score, modified Rankin scale score, hemorrhage rate, all-cause mortality rate, and the rate of favorable outcomes at 90 ± 7 days.

RESULTS: Ninety-four participants were enrolled in this study, with both the control and experimental groups initiating intravenous injection of rt-PA at a median time of 29 min. For the experimental group, the median time for arterial puncture was 123 min. Baseline data for both groups were similar (P > 0.05). Hemorrhagic transformation occurred in 24.47 % (23 patients), with a lower intracranial hemorrhage rate observed in the experimental group compared to the control group (15.2 % vs 33.3 %, P < 0.05). Asymptomatic hemorrhage rates were 8.7 % for the experimental group and 12.5 % for the control group, with no hemorrhage detected in other locations. Post-treatment median NIHSS scores were lower in the experimental group than in the control group (7 vs 9, P < 0.05), but short-term NIHSS scores were similar (P > 0.05). A higher proportion of patients in the experimental group achieved favorable outcomes compared to the control group (87.0 % vs 43.8 %, P < 0.05).

CONCLUSION: In patients with acute ischemic stroke with an onset time of ≤6 h and no major intracranial vessel occlusion, combining rt-PA intravenous thrombolysis with intra-arterial thrombolysis via a microcatheter might yield superior functional outcomes.

RevDate: 2024-05-07

Soler A, Giraldo E, M Molinas (2024)

EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels.

Brain informatics, 11(1):11.

The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the electric potential from the brain to the scalp and introduces spatial mixing to the signals. EEG source imaging (ESI) techniques can be applied to alleviate these issues and enhance the spatial segregation of information. Despite this potential solution, the use of ESI has not been extensively applied in BCI systems, largely due to accuracy concerns over reconstruction accuracy when using low-density EEG (ldEEG), which is commonly used in BCIs. To overcome these accuracy issues in low channel counts, recent studies have proposed reducing the number of EEG channels based on optimized channel selection. This work presents an evaluation of the spatial and temporal accuracy of ESI when applying optimized channel selection towards ldEEG number of channels. For this, a simulation study of source activity related to hand movement has been performed using as a starting point an EEG system with 339 channels. The results obtained after optimization show that the activity in the concerned areas can be retrieved with a spatial accuracy of 3.99, 10.69, and 14.29 mm (localization error) when using 32, 16, and 8 channel counts respectively. In addition, the use of optimally selected electrodes has been validated in a motor imagery classification task, obtaining a higher classification performance when using 16 optimally selected channels than 32 typical electrode distributions under 10-10 system, and obtaining higher classification performance when combining ESI methods with the optimal selected channels.

RevDate: 2024-05-03

Ma X, Chen W, Pei Z, et al (2024)

Attention-based convolutional neural network with multi-modal temporal information fusion for motor imagery EEG decoding.

Computers in biology and medicine, 175:108504 pii:S0010-4825(24)00588-2 [Epub ahead of print].

Convolutional neural network (CNN) has been widely applied in motor imagery (MI)-based brain computer interface (BCI) to decode electroencephalography (EEG) signals. However, due to the limited perceptual field of convolutional kernel, CNN only extracts features from local region without considering long-term dependencies for EEG decoding. Apart from long-term dependencies, multi-modal temporal information is equally important for EEG decoding because it can offer a more comprehensive understanding of the temporal dynamics of neural processes. In this paper, we propose a novel deep learning network that combines CNN with self-attention mechanism to encapsulate multi-modal temporal information and global dependencies. The network first extracts multi-modal temporal information from two distinct perspectives: average and variance. A shared self-attention module is then designed to capture global dependencies along these two feature dimensions. We further design a convolutional encoder to explore the relationship between average-pooled and variance-pooled features and fuse them into more discriminative features. Moreover, a data augmentation method called signal segmentation and recombination is proposed to improve the generalization capability of the proposed network. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and BCI Competition IV-2b (BCIC-IV-2b) datasets show that our proposed method outperforms the state-of-the-art methods and achieves 4-class average accuracy of 85.03% on the BCIC-IV-2a dataset. The proposed method implies the effectiveness of multi-modal temporal information fusion in attention-based deep learning networks and provides a new perspective for MI-EEG decoding. The code is available at

RevDate: 2024-05-03

Valencia D, Mercier PP, A Alimohammad (2024)

An Efficient Brain-Switch for Asynchronous Brain-Computer Interfaces.

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

Intracortical brain computer interfaces (iBCIs) utilizing extracellular recordings mainly employ in vivo signal processing application-specific integrated circuits (ASICs) to detect action potentials (spikes). Conventionally, "brain-switches" based on spiking activity have been employed to realize asynchronous (self-paced) iBCIs, estimating when the user involves in the underlying BCI task. Several studies have demonstrated that local field potentials (LFPs) can effectively replace action potentials, drastically reducing the power consumption and processing requirements of in vivo ASICs. This article presents the first LFP-based brain-switch design and implementation using gated recurrent neural networks (RNNs). Compared to the previously reported brain-switches, our design requires no exhaustive learning phase for the estimation of optimal recording channels or frequency band selection, making it more applicable to practical asynchronous iBCIs. The synthesized ASIC of the designed in vivo LFP-based feature extraction unit, in a standard 180-nm CMOS process, occupies only 0.09 mm[2] of silicon area, and the post place-and-route synthesis results indicate that it consumes 91.87 nW of power while operating at 2 kHz. Compared to the previously published ASICs, the proposed LFP-based brain-switch consumes the least power for in vivo digital signal processing and achieves comparable state estimation performance to that of spike-based brain-switches.

RevDate: 2024-05-03

Li Y, Fang Y, Li K, et al (2024)

Morphological Tracing and Functional Identification of Monosynaptic Connections in the Brain: A Comprehensive Guide.

Neuroscience bulletin [Epub ahead of print].

Behavioral studies play a crucial role in unraveling the mechanisms underlying brain function. Recent advances in optogenetics, neuronal typing and labeling, and circuit tracing have facilitated the dissection of the neural circuitry involved in various important behaviors. The identification of monosynaptic connections, both upstream and downstream of specific neurons, serves as the foundation for understanding complex neural circuits and studying behavioral mechanisms. However, the practical implementation and mechanistic understanding of monosynaptic connection tracing techniques and functional identification remain challenging, particularly for inexperienced researchers. Improper application of these methods and misinterpretation of results can impede experimental progress and lead to erroneous conclusions. In this paper, we present a comprehensive description of the principles, specific operational details, and key steps involved in tracing anterograde and retrograde monosynaptic connections. We outline the process of functionally identifying monosynaptic connections through the integration of optogenetics and electrophysiological techniques, providing practical guidance for researchers.

RevDate: 2024-05-03

Pang B, Peng Y, Gao J, et al (2024)

Semi-supervised bipartite graph construction with active EEG sample selection for emotion recognition.

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

Electroencephalogram (EEG) signals are derived from the central nervous system and inherently difficult to camouflage, leading to the recent popularity of EEG-based emotion recognition. However, due to the non-stationary nature of EEG, inter-subject variabilities become obstacles for recognition models to well adapt to different subjects. In this paper, we propose a novel approach called semi-supervised bipartite graph construction with active EEG sample selection (SBGASS) for cross-subject emotion recognition, which offers two significant advantages. Firstly, SBGASS adaptively learns a bipartite graph to characterize the underlying relationships between labeled and unlabeled EEG samples, effectively implementing the semantic connection for samples from different subjects. Secondly, we employ active sample selection technique in this paper to reduce the impact of negative samples (outliers or noise in the data) on bipartite graph construction. Drawing from the experimental results with the SEED-IV data set, we have gained the following three insights. (1) SBGASS actively rejects negative labeled samples, which helps mitigate the impact of negative samples when constructing the optimal bipartite graph and improves the model performance. (2) Through the learned optimal bipartite graph in SBGASS, the transferability of labeled EEG samples is quantitatively analyzed, which exhibits a decreasing tendency as the distance between each labeled sample and the corresponding class centroid increases. (3) Besides the improved recognition accuracy, the spatial-frequency patterns in emotion recognition are investigated by the acquired projection matrix.

RevDate: 2024-05-03

Huang X, Xue Z, Zhang D, et al (2024)

Pinpointing Fat Molecules: Advances in Coherent Raman Scattering Microscopy for Lipid Metabolism.

Analytical chemistry [Epub ahead of print].

RevDate: 2024-05-03

Rybář M, Poli R, I Daly (2024)

Corrigendum: Decoding of semantic categories of imagined concepts of animals and tools in fNIRS (2021J. Neural Eng. 18 046035).

Journal of neural engineering, 21(2):.

RevDate: 2024-05-04

Eldawlatly S (2024)

On the role of generative artificial intelligence in the development of brain-computer interfaces.

BMC biomedical engineering, 6(1):4.

Since their inception more than 50 years ago, Brain-Computer Interfaces (BCIs) have held promise to compensate for functions lost by people with disabilities through allowing direct communication between the brain and external devices. While research throughout the past decades has demonstrated the feasibility of BCI to act as a successful assistive technology, the widespread use of BCI outside the lab is still beyond reach. This can be attributed to a number of challenges that need to be addressed for BCI to be of practical use including limited data availability, limited temporal and spatial resolutions of brain signals recorded non-invasively and inter-subject variability. In addition, for a very long time, BCI development has been mainly confined to specific simple brain patterns, while developing other BCI applications relying on complex brain patterns has been proven infeasible. Generative Artificial Intelligence (GAI) has recently emerged as an artificial intelligence domain in which trained models can be used to generate new data with properties resembling that of available data. Given the enhancements observed in other domains that possess similar challenges to BCI development, GAI has been recently employed in a multitude of BCI development applications to generate synthetic brain activity; thereby, augmenting the recorded brain activity. Here, a brief review of the recent adoption of GAI techniques to overcome the aforementioned BCI challenges is provided demonstrating the enhancements achieved using GAI techniques in augmenting limited EEG data, enhancing the spatiotemporal resolution of recorded EEG data, enhancing cross-subject performance of BCI systems and implementing end-to-end BCI applications. GAI could represent the means by which BCI would be transformed into a prevalent assistive technology, thereby improving the quality of life of people with disabilities, and helping in adopting BCI as an emerging human-computer interaction technology for general use.

RevDate: 2024-05-02

Chaudhary P, Dhankhar N, Singhal A, et al (2024)

A two-stage transformer based network for motor imagery classification.

Medical engineering & physics pii:S1350-4533(24)00055-9 [Epub ahead of print].

Brain-computer interfaces (BCIs) are used to understand brain functioning and develop therapies for neurological and neurodegenerative disorders. Therefore, BCIs are crucial in rehabilitating motor dysfunction and advancing motor imagery applications. For motor imagery, electroencephalogram (EEG) signals are used to classify the subject's intention of moving a body part without actually moving it. This paper presents a two-stage transformer-based architecture that employs handcrafted features and deep learning techniques to enhance the classification performance on benchmarked EEG signals. Stage-1 is built on parallel convolution based EEGNet, multi-head attention, and separable temporal convolution networks for spatiotemporal feature extraction. Further, for enhanced classification, in stage-2, additional features and embeddings extracted from stage-1 are used to train TabNet. In addition, a novel channel cluster swapping data augmentation technique is also developed to handle the issue of limited samples for training deep learning architectures. The developed two-stage architecture offered an average classification accuracy of 88.5 % and 88.3 % on the BCI Competition IV-2a and IV-2b datasets, respectively, which is approximately 3.0 % superior over similar recent reported works.

RevDate: 2024-05-04

Zhang W, Jiang M, Teo KAC, et al (2024)

Revealing the spatiotemporal brain dynamics of covert speech compared with overt speech: A simultaneous EEG-fMRI study.

NeuroImage, 293:120629 pii:S1053-8119(24)00124-1 [Epub ahead of print].

Covert speech (CS) refers to speaking internally to oneself without producing any sound or movement. CS is involved in multiple cognitive functions and disorders. Reconstructing CS content by brain-computer interface (BCI) is also an emerging technique. However, it is still controversial whether CS is a truncated neural process of overt speech (OS) or involves independent patterns. Here, we performed a word-speaking experiment with simultaneous EEG-fMRI. It involved 32 participants, who generated words both overtly and covertly. By integrating spatial constraints from fMRI into EEG source localization, we precisely estimated the spatiotemporal dynamics of neural activity. During CS, EEG source activity was localized in three regions: the left precentral gyrus, the left supplementary motor area, and the left putamen. Although OS involved more brain regions with stronger activations, CS was characterized by an earlier event-locked activation in the left putamen (peak at 262 ms versus 1170 ms). The left putamen was also identified as the only hub node within the functional connectivity (FC) networks of both OS and CS, while showing weaker FC strength towards speech-related regions in the dominant hemisphere during CS. Path analysis revealed significant multivariate associations, indicating an indirect association between the earlier activation in the left putamen and CS, which was mediated by reduced FC towards speech-related regions. These findings revealed the specific spatiotemporal dynamics of CS, offering insights into CS mechanisms that are potentially relevant for future treatment of self-regulation deficits, speech disorders, and development of BCI speech applications.

RevDate: 2024-05-07

Wen X, Yang M, Qi S, et al (2024)

Automated individual cortical parcellation via consensus graph representation learning.

NeuroImage, 293:120616 pii:S1053-8119(24)00111-3 [Epub ahead of print].

Cortical parcellation plays a pivotal role in elucidating the brain organization. Despite the growing efforts to develop parcellation algorithms using functional magnetic resonance imaging, achieving a balance between intra-individual specificity and inter-individual consistency proves challenging, making the generation of high-quality, subject-consistent cortical parcellations particularly elusive. To solve this problem, our paper proposes a fully automated individual cortical parcellation method based on consensus graph representation learning. The method integrates spectral embedding with low-rank tensor learning into a unified optimization model, which uses group-common connectivity patterns captured by low-rank tensor learning to optimize subjects' functional networks. This not only ensures consistency in brain representations across different subjects but also enhances the quality of each subject's representation matrix by eliminating spurious connections. More importantly, it achieves an adaptive balance between intra-individual specificity and inter-individual consistency during this process. Experiments conducted on a test-retest dataset from the Human Connectome Project (HCP) demonstrate that our method outperforms existing methods in terms of reproducibility, functional homogeneity, and alignment with task activation. Extensive network-based comparisons on the HCP S900 dataset reveal that the functional network derived from our cortical parcellation method exhibits greater capabilities in gender identification and behavior prediction than other approaches.

RevDate: 2024-05-03

Polyakov D, Robinson PA, Muller EJ, et al (2024)

Recruiting neural field theory for data augmentation in a motor imagery brain-computer interface.

Frontiers in robotics and AI, 11:1362735.

We introduce a novel approach to training data augmentation in brain-computer interfaces (BCIs) using neural field theory (NFT) applied to EEG data from motor imagery tasks. BCIs often suffer from limited accuracy due to a limited amount of training data. To address this, we leveraged a corticothalamic NFT model to generate artificial EEG time series as supplemental training data. We employed the BCI competition IV '2a' dataset to evaluate this augmentation technique. For each individual, we fitted the model to common spatial patterns of each motor imagery class, jittered the fitted parameters, and generated time series for data augmentation. Our method led to significant accuracy improvements of over 2% in classifying the "total power" feature, but not in the case of the "Higuchi fractal dimension" feature. This suggests that the fit NFT model may more favorably represent one feature than the other. These findings pave the way for further exploration of NFT-based data augmentation, highlighting the benefits of biophysically accurate artificial data.

RevDate: 2024-05-03

Uszko JM, Eichhorn SJ, Patil AJ, et al (2024)

Detonation of fulminating gold produces heterogeneous gold nanoparticles.

Nanoscale advances, 6(9):2231-2233.

Fulminating gold, the first high-explosive compound to be discovered, disintegrates into a mysterious cloud of purple smoke, the nature of which has been speculated upon since its discovery in the 15th century. In this work, we show that the colour of the smoke is due to the presence of gold nanoparticles.

RevDate: 2024-05-03

Yao X, Li M, He S, et al (2024)

Kirigami-Triggered Spoof Plasmonic Interconnects for Radiofrequency Elastronics.

Research (Washington, D.C.), 7:0367.

The flexible and conformal interconnects for electronic systems as a potential signal transmission device have great prospects in body-worn or wearable applications. High-efficiency wave propagation and conformal structure deformation around human body at radio communication are still confronted with huge challenges due to the lack of methods to control the wave propagation and achieve the deformable structure simultaneously. Here, inspired by the kirigami technology, a new paradigm to construct spoof plasmonic interconnects (SPIs) that support radiofrequency (RF) surface plasmonic transmission is proposed, together with high elasticity, strong robustness, and multifunction performance. Leveraging the strong field-confinement characteristic of spoof surface plasmons polaritons, the Type-I SPI opens its high-efficiency transmission band after stretching from a simply connected metallic surface. Meanwhile, the broadband transmission of the kirigami-based SPI exhibits strong robustness and excellent stability undergoing complex deformations, i.e., bending, twisting, and stretching. In addition, the prepared Type-II SPI consisting of 2 different subunit cells can achieve band-stop transmission characteristics, with its center frequency dynamically tunable by stretching the buckled structure. Experimental measurements verify the on-off switching performance in kirigami interconnects triggered by stretching. Overcoming the mechanical limitation of rigid structure with kirigami technology, the designer SPIs exhibit high stretchability through out-of-plane structure deformation. Such kirigami-based interconnects can improve the elastic functionality of wearable RF electronics and offer high compatibility to large body motion in future body network systems.

RevDate: 2024-05-04

Zhang Y, Lv Q, Yin Y, et al (2024)

Research in China about the biological mechanisms that potentially link socioenvironmental changes and mental health: a scoping review.

The Lancet regional health. Western Pacific, 45:100610.

China's rapid socioeconomic development since 1990 makes it a fitting location to summarise research about how biological changes associated with socioenvironmental changes affect population mental health and, thus, lay the groundwork for subsequent, more focused studies. An initial search identified 308 review articles in the international literature about biomarkers associated with 12 common mental health disorders. We then searched for studies conducted in China that assessed the association of the identified mental health related-biomarkers with socioenvironmental factors in English-language and Chinese-language databases. We located 1330 articles published between 1 January 1990 and 1 August 2021 that reported a total of 3567 associations between 56 specific biomarkers and 11 socioenvironmental factors: 3156 (88·5%) about six types of environmental pollution, 381 (10·7%) about four health-related behaviours (diet, physical inactivity, internet misuse, and other lifestyle factors), and 30 (0·8%) about socioeconomic inequity. Only 245 (18·4%) of the papers simultaneously considered the possible effect of the biomarkers on mental health conditions; moreover, most of these studies assessed biomarkers in animal models of mental disorders, not human subjects. Among the 245 papers, mental health conditions were linked with biomarkers of environmental pollution in 188 (76·7%), with biomarkers of health-related behaviours in 48 (19·6%), and with biomarkers of socioeconomic inequality in 9 (3·7%). The 604 biomarker-mental health condition associations reported (107 in human subjects and 497 in animal models) included 379 (62·7%) about cognitive functioning, 117 (19·4%) about anxiety, 56 (9·3%) about depression, 21 (3·5%) about neurodevelopmental conditions, and 31 (5·1%) about neurobehavioural symptoms. Improved understanding of the biological mechanisms linking socioenvironmental changes to community mental health will require expanding the range of socioenvironmental factors considered, including mental health outcomes in more of the studies about the association of biomarkers with socioenvironmental factors, and increasing the proportion of studies that assess mental health outcomes in humans.

RevDate: 2024-05-04

Lotun S, Lamarche VM, Matran-Fernandez A, et al (2024)

Author Correction: People perceive parasocial relationships to be effective at fulfilling emotional needs.

Scientific reports, 14(1):9986 pii:10.1038/s41598-024-60558-w.

RevDate: 2024-05-01

Xu K, Yang Y, Ding J, et al (2024)

Spatially Precise Genetic Engineering at the Electrode-Tissue Interface.

Advanced materials (Deerfield Beach, Fla.) [Epub ahead of print].

The interface between electrodes and neural tissues plays a pivotal role in determining the efficacy and fidelity of neural activity recording and modulation. While considerable efforts have been made to improve the electrode-tissue interface, the majority of studies have primarily concentrated on the development of biocompatible neural electrodes through abiotic materials and structural engineering. In this study, we present an approach that seamlessly integrates abiotic and biotic engineering principles into the electrode-tissue interface. Specifically, we combine ultraflexible neural electrodes with short hairpin RNAs (shRNAs) designed to silence the expression of endogenous genes within neural tissues. Our system facilitates shRNA-mediated knockdown of PTEN and PTBP1, two essential genes associated in neural survival/growth and neurogenesis, within specific cell populations located at the electrode-tissue interface. Additionally, we demonstrate that the downregulation of PTEN in neurons can result in an enlargement of neuronal cell bodies at the electrode-tissue interface. Furthermore, our system enables long-term monitoring of neuronal activities following PTEN knockdown in a mouse model of Parkinson's disease and traumatic brain injury. Our system provides a versatile approach for genetically engineering the electrode-tissue interface with unparalleled precision, paving the way for the development of regenerative electronics and next-generation brain-machine interfaces. This article is protected by copyright. All rights reserved.

RevDate: 2024-05-01

Duan T, Wang Z, Li F, et al (2024)

Online continual decoding of streaming EEG signal with a balanced and informative memory buffer.

Neural networks : the official journal of the International Neural Network Society, 176:106338 pii:S0893-6080(24)00262-4 [Epub ahead of print].

Electroencephalography (EEG) based Brain Computer Interface (BCI) systems play a significant role in facilitating how individuals with neurological impairments effectively interact with their environment. In real world applications of BCI system for clinical assistance and rehabilitation training, the EEG classifier often needs to learn on sequentially arriving subjects in an online manner. As patterns of EEG signals can be significantly different for different subjects, the EEG classifier can easily erase knowledge of learnt subjects after learning on later ones as it performs decoding in online streaming scenario, namely catastrophic forgetting. In this work, we tackle this problem with a memory-based approach, which considers the following conditions: (1) subjects arrive sequentially in an online manner, with no large scale dataset available for joint training beforehand, (2) data volume from the different subjects could be imbalanced, (3) decoding difficulty of the sequential streaming signal vary, (4) continual classification for a long time is required. This online sequential EEG decoding problem is more challenging than classic cross subject EEG decoding as there is no large-scale training data from the different subjects available beforehand. The proposed model keeps a small balanced memory buffer during sequential learning, with memory data dynamically selected based on joint consideration of data volume and informativeness. Furthermore, for the more general scenarios where subject identity is unknown to the EEG decoder, aka. subject agnostic scenario, we propose a kernel based subject shift detection method that identifies underlying subject changes on the fly in a computationally efficient manner. We develop challenging benchmarks of streaming EEG data from sequentially arriving subjects with both balanced and imbalanced data volumes, and performed extensive experiments with a detailed ablation study on the proposed model. The results show the effectiveness of our proposed approach, enabling the decoder to maintain performance on all previously seen subjects over a long period of sequential decoding. The model demonstrates the potential for real-world applications.

RevDate: 2024-05-02

Ma D, Jin X, Sun S, et al (2024)

Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning.

National science review, 11(5):nwae102.

Spiking neural networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture, which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind on the neuron scale. The experimental results showed that the code density was improved by up to 28.3× in Darwin3, and that the neuron core fan-in and fan-out were improved by up to 4096× and 3072× by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8× and 200.8× when mapping convolutional spiking neural networks onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.

RevDate: 2024-05-02

Forenzo D, Zhu H, Shanahan J, et al (2024)

Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface.

PNAS nexus, 3(4):pgae145.

Brain-computer interfaces (BCI) using electroencephalography provide a noninvasive method for users to interact with external devices without the need for muscle activation. While noninvasive BCIs have the potential to improve the quality of lives of healthy and motor-impaired individuals, they currently have limited applications due to inconsistent performance and low degrees of freedom. In this study, we use deep learning (DL)-based decoders for online continuous pursuit (CP), a complex BCI task requiring the user to track an object in 2D space. We developed a labeling system to use CP data for supervised learning, trained DL-based decoders based on two architectures, including a newly proposed adaptation of the PointNet architecture, and evaluated the performance over several online sessions. We rigorously evaluated the DL-based decoders in a total of 28 human participants, and found that the DL-based models improved throughout the sessions as more training data became available and significantly outperformed a traditional BCI decoder by the last session. We also performed additional experiments to test an implementation of transfer learning by pretraining models on data from other subjects, and midsession training to reduce intersession variability. The results from these experiments showed that pretraining did not significantly improve performance, but updating the models' midsession may have some benefit. Overall, these findings support the use of DL-based decoders for improving BCI performance in complex tasks like CP, which can expand the potential applications of BCI devices and help to improve the quality of lives of healthy and motor-impaired individuals.

RevDate: 2024-05-02
CmpDate: 2024-04-30

Li X, Wang D, Zhang B, et al (2024)

[A review on electroencephalogram based channel selection].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 41(2):398-405.

The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system.

RevDate: 2024-05-01

Liu X, Gong Y, Jiang Z, et al (2024)

Flexible high-density microelectrode arrays for closed-loop brain-machine interfaces: a review.

Frontiers in neuroscience, 18:1348434.

Flexible high-density microelectrode arrays (HDMEAs) are emerging as a key component in closed-loop brain-machine interfaces (BMIs), providing high-resolution functionality for recording, stimulation, or both. The flexibility of these arrays provides advantages over rigid ones, such as reduced mismatch between interface and tissue, resilience to micromotion, and sustained long-term performance. This review summarizes the recent developments and applications of flexible HDMEAs in closed-loop BMI systems. It delves into the various challenges encountered in the development of ideal flexible HDMEAs for closed-loop BMI systems and highlights the latest methodologies and breakthroughs to address these challenges. These insights could be instrumental in guiding the creation of future generations of flexible HDMEAs, specifically tailored for use in closed-loop BMIs. The review thoroughly explores both the current state and prospects of these advanced arrays, emphasizing their potential in enhancing BMI technology.


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 large collection of user-selected side-by-side timelines (e.g., all science vs. all other categories, or arts and culture vs. world history), designed to provide a comparative context for appreciating world events.


Biographical information about many key scientists (e.g., Walter Sutton).

Selected Bibliographies

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

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