Brain-computer Interface Research Articles

A multiple session dataset of NIRS recordings from stroke patients controlling brain–computer interface

This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. Fifteen stroke patients completed a total of 237 motor imagery brain–computer interface (BCI) sessions. The BCI was controlled by imagined hand movements; visual feedback was presented based on the real–time data classification results. We provide the experimental records, patient demographic profiles, clinical scores (including ARAT and Fugl–Meyer), online BCI performance, and a simple analysis of hemodynamic response. We assume that this dataset can be useful for evaluating the effectiveness of various near–infrared spectroscopy signal processing and analysis techniques in patients with cerebrovascular accidents.

A Comprehensive Analysis of the Design of Brain-Computer Interface Systems Utilizing Electroencephalography as a Means of Measurement: A Survey

A Comprehensive Analysis of the Design of Brain-Computer Interface Systems Utilizing Electroencephalography as a Means of Measurement: A Survey

A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling.

Brain-computer interfaces (BCIs) can restore communication for movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command click detectors provide a basic yet highly functional capability. We sought to test the performance and long-term stability of click decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis. We trained the participant's click detector using a small amount of training data (<44 min across 4 days) collected up to 21 days prior to BCI use, and then tested it over a period of 90 days without any retraining or updating. Using a click detector to navigate a switch scanning speller interface, the study participant can maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation can interrupt usage of a fixed model, a new click detector can achieve comparable performance despite being trained with even less data (<15 min, within 1 day). These results demonstrate that a click detector can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users.

Suggestion of a hybrid computational model to optimize EEG motor imagery signals classification

Brain-computer interfaces (BCIs) have proven to be innovative and promising technologies for human-machine interaction. However, distinguishing with relative precision between EEG motor signals generated by real or imagined movements of the left and right hands has still been a major challenge. By analyzing brain electrical signals and the processes of reading, processing, and interpreting data, the hybrid model suggested in this dissertation contributed to greater effectiveness of the algorithms, with accuracy above that normally found in most models present in the most recent literature. The experimental and observational research carried out in the creation of the methodology applied in this dissertation ensures the replication of automation methods, techniques, and algorithms, machine learning, and more specifically, in data preparation, feature extraction, signal classification, and obtaining results with greater accuracy for the implementation of brain-computer interfaces, BCIs. This dissertation opens new possibilities for future research and deepening of techniques, tools, and new experiments. With a premise to create personalized BCIs and trained specifically for individual pattern recognition, as they have shown greater potential results. Each human being is unique and has singular characteristics that distinguish them from all other beings.

A Wireless Bi-Directional Brain–Computer Interface Supporting Both Bluetooth and Wi-Fi Transmission

Wireless neural signal transmission is essential for both neuroscience research and neural disorder therapies. However, conventional wireless systems are often constrained by low sampling rates, limited channel counts, and their support of only a single transmission mode. Here, we developed a wireless bi-directional brain–computer interface system featuring dual transmission modes. This system supports both low-power Bluetooth transmission and high-sampling-rate Wi-Fi transmission, providing flexibility for various application scenarios. The Bluetooth mode, with a maximum sampling rate of 14.4 kS/s, is well suited for detecting low-frequency signals, as demonstrated by both in vitro recordings of signals from 10 to 50 Hz and in vivo recordings of 16-channel local field potentials in mice. More importantly, the Wi-Fi mode, offering a maximum sampling rate of 56.8 kS/s, is optimized for recording high-frequency signals. This capability was validated through in vitro recordings of signals from 500 to 2000 Hz and in vivo recordings of single-neuron spike firings with amplitudes reaching hundreds of microvolts and high signal-to-noise ratios. Additionally, the system incorporates a wireless stimulation function capable of delivering current pulses up to 2.55 mA, with adjustable pulse width and polarity. Overall, this dual-mode system provides an efficient and flexible solution for both neural recording and stimulation applications.

Sensory-substitution based sound perception using a spinal computer–brain interface

Sensory substitution offers a promising approach to restore lost sensory functions. Here we show that spinal cord stimulation (SCS), typically used for chronic pain management, can potentially serve as a novel auditory sensory substitution device. We recruited 13 patients undergoing SCS implantation and translated everyday sound samples into personalized SCS patterns during their trial phase. In a sound identification task—where chance-level performance was 33.3%—participants (n=8\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n=8$$\\end{document}) achieved a mean accuracy of 72.8% using only SCS input. We observed a weak positive correlation between stimulation bitrate and identification accuracy. A follow-up discrimination task (n=5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n=5$$\\end{document}) confirmed that reduced bitrates significantly impaired participants’ ability to distinguish between consecutive SCS patterns, indicating effective processing of additional information at higher bitrates. These findings demonstrate the feasibility of using existing SCS technology to create a novel neural interface for a sound prosthesis. Our results pave the way for future research to enhance stimulation fidelity, assess long-term training effects, and explore integration with other auditory aids for comprehensive hearing rehabilitation.

Thought-Controlled Computer Applications: A Brain–Computer Interface System for Severe Disability Support

This study introduces an integrated computational environment that leverages Brain–Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human–Computer interfaces to provide a more intuitive and accessible experience. The proposed system offers four key applications to users controlled by four thoughts: an email client, a web browser, an e-learning tool, and both command-line and graphical user interfaces for managing computer resources. The BCI framework translates ElectroEncephaloGraphy (EEG) signals into commands or events using advanced signal processing and machine learning techniques. These identified commands are then processed by an integrative strategy that triggers the appropriate actions and provides real-time feedback on the screen. Our study shows that our framework achieved an 82% average classification accuracy using four distinct thoughts of 62 subjects and a 95% recognition rate for P300 signals from two users, highlighting its effectiveness in translating brain signals into actionable commands. Unlike most existing prototypes that rely on visual stimulation, our system is controlled by thought, inducing brain activity to manage the system’s Application Programming Interfaces (APIs). It switches to P300 mode for a virtual keyboard and text input. The proposed BCI system significantly improves the ability of individuals with severe disabilities to interact with various applications and manage computer resources. Our approach demonstrates superior performance in terms of classification accuracy and signal recognition compared to existing methods.

Multimodal human computer interaction of wheelchairs supporting lower limb active rehabilitation

Currently, an important challenge in stroke rehabilitation is how to effectively restore motor functions of lower limbs. This paper presents multimodal human computer interaction (HCI) of wheelchairs supporting lower limb active rehabilitation. First, multimodal HCI incorporating motor imagery electroencephalography (EEG), electromyography (EMG) and speech is designed. Second, prototype supporting wheelchair active rehabilitation method is illustrated in details. Third, the preliminary brain-computer interfaces (BCI) and speech recognition task experiments are carried out respectively, and the results are obtained. Finally, discussion is conducted and conclusion is drawn. This study has important practical significance in auxiliary movements and neurorehabilitation for stroke patients.

Brain-computer interfaces based on near-infrared spectroscopy and electroencephalography registration in post-stroke rehabilitation: a comparative study

Motor imagery training under the control of a brain-computer interface (BCI) facilitates motor recovery after stroke. The efficacy of BCI based on electroencephalography (EEG-BCI) has been confirmed by several meta-analyses, but a more convenient and noise-resistant method of near-infrared spectroscopy in the BCI circuit (NIRS-BCI) has been practically unexamined; comparisons of the two types of BCI have not been performed.Objective: to compare the control accuracy and clinical efficacy of NIRS-BCI and EEG-IMC in post-stroke rehabilitation.Material and methods. The NIRS-BCI group consisted of patients from an uncontrolled study (n=15; 9 men and 6 women; age – 59.0 [49.0; 70.0] years; stroke duration – 7.0 [2.0; 10.0] months; upper limb paresis – 47.0 [35.0; 54.0] points on the Fugl-Meyer Assessment for motor function evaluation of the upper limb – FM-UL). The EEG-IMC group was formed from the main group of the randomized controlled trial “iMove” (n=17; 13 men and 4 women; age – 53.0 [49.0; 70.0] years; stroke duration – 10.0 [6.0; 13.0] months; upper limb paresis – 33.0 [12.0; 53.0] points on the FM-UL). Patients participated in a comprehensive rehabilitation program supplemented by BCI-guided movement imagery training (average of 9 training sessions).Results. Median of average BCI control rates achieved by the patients was 46.4 [44.2; 60.4]% in the NIRS group and 40.0 [35.7; 45.1]% in the EEG group (p=0.004). For the NIRS-BCI group, the median of the maximum BCI control accuracy achieved was 66.2 [56.4; 73.7]%, for EEGBCI – 50.6 [43.0; 62.3]% (p=0.006). The proportion of patients who achieved a clinically significant improvement according ARAT and the proportion of patients who achieved a clinically significant improvement according FM-UL were comparable in both groups. The NIRS-BCI group showed greater improvement in motor function compared to the EEG-BCI group according to Action Research Arm Test (ARAT; an increase of 5.0 [4.0; 8.0] points compared to an increase of 1.0 [0.0; 3.0] points; p=0.008), but not according to FM-UL scale (an increase of 5.0 [1.0; 10.0] and 4.0 [2.0; 5.0] points, respectively; p=0.455).Conclusion. NIRS-BCI has an advantage in control accuracy and ease of use in clinical practice. Achieving higher control accuracy of BCI provides additional opportunities for the use of game feedback scenarios to increase patient motivation.

A brief survey on human activity recognition using motor imagery of EEG signals.

Human being's biological processes and psychological activities are jointly connected to the brain. So, the examination of human activity is more significant for the well-being of humans. There are various models for brain activity detection considering neuroimaging for attaining decreased time requirement, increased control commands, and enhanced accuracy. Motor Imagery (MI)-based Brain-Computer Interface (BCI) systems create a way in which the brain can interact with the environment by processing Electroencephalogram (EEG) signals. Human Activity Recognition (HAR) deals with identifying the physiological activities of human beings based on sensory signals. This survey reviews the different methods available for HAR based on MI-EEG signals. A total of 50 research articles based on HAR from EEG signals are considered in this survey. This survey discusses the challenges faced by various techniques for HAR. Moreover, the papers are assessed considering various parameters, techniques, publication year, performance metrics, utilized tools, employed databases, etc. There were many techniques developed to solve the problem of HAR and they are classified as Machine Learning (ML) and Deep Learning (DL)models. At last, the research gaps and limitations of the techniques were discussed that contribute to developing an effective HAR.

EEGER – A Model for Recognition of Human Emotion Using Brain Signal

Emotions significantly impact human thinking, judgment, health, and communication. EEG-based emotion detection has advanced with the use of Brain-Computer Interface (BCI) technology, proving more effective than other physiological data. Despite progress in affective computing, emotion recognition remains a challenge. However, it's increasingly common in brain–machine interfaces, and research shows EEG brain waves are valuable in identifying emotional states. This research introduces a novel automated system for emotion recognition using deep learning techniques on EEG data collected from the GAMEEMO dataset, where participants played emotional assessment games. The system is designed to identify four emotions experienced during gameplay. The proposed model, called EEGER, was trained exclusively on EEG signals and demonstrated a 99.99% accuracy with minimal computational time. Key to its efficiency is the use of LSTM (Long Short-Term Memory) classifiers, which simplify the process by automatically extracting relevant features. The system was tested across different learning rates and epoch values, showing that 10 epochs with a learning rate of 0.0001 were sufficient to achieve the best accuracy. EEGER was also compared with other methods like K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Adaptive Boosting (AdaBoost), outperforming them in both accuracy and efficiency. These findings suggest that EEGER offers a promising new approach to EEG-based emotion recognition, optimizing performance with lower complexity and computation time.

Motor-Related EEG Analysis Using a Pole Tracking Approach.

This study introduces an alternative approach to electroencephalography (EEG) time-frequency analysis based on time-varying autoregressive (TV-AR) models in a cascade configuration to independently monitor key EEG spectral components. The method is evaluated for its neurophysiological interpretation and effectiveness in motor-related brain-computer interface (BCI) applications. Specifically, we assess the ability of the tracked EEG poles to discriminate between rest, movement execution (ME) and movement imagination (MI) in healthy subjects, as well as movement attempts (MA) in individuals with spinal cord injury (SCI). Our results show that pole tracking effectively captures broad changes in EEG dynamics, such as transitions between rest and movement-related states. It outperformed traditional EEG-based features, increasing detection accuracy for ME by an average of 4.1% (with individual improvements reaching as high as 15%) and MI by an average of 4.5% (up to 13.8%) compared to time-domain low-frequency EEG features. Similarly, compared to alpha/beta band power, the method improved ME detection by an average of 5.9% (up to 10.4%) and MI by an average of 4.3% (up to 10.2%), with results averaged across 15 healthy participants. In one participant with SCI, pole tracking improved MA detection by 12.9% over low-frequency EEG features and 4.8% over alpha/beta band power. However, its ability to distinguish finer movement details within specific movement types was limited. Additionally, the temporal evolution of the extracted pole tracking features revealed event-related desynchronization phenomena, typically observed during ME, MA and MI, as well as increases in frequency, which are of neurophysiological interest.

Optogenetic stimulation recruits cortical neurons in a morphology-dependent manner.

Single-photon optogenetics enables precise, cell-type-specific modulation of neuronal circuits, making it a crucial tool in neuroscience. Its miniaturization in the form of fully implantable wide-field stimulator arrays enables long-term interrogation of cortical circuits and bares promise for Brain-Machine Interfaces for sensory and motor function restoration. However, achieving selective activation of functional cortical representations poses a challenge, as studies show that targeted optogenetic stimulation results in activity spread beyond one functional domain. While recurrent network mechanisms contribute to activity spread, here we demonstrate with detailed simulations of isolated pyramidal neurons from cat of unknown sex that already neuron morphology causes a complex spread of optogenetic activity at the scale of one cortical column. Since the shape of a neuron impacts its optogenetic response, we find that a single stimulator at the cortical surface recruits a complex spatial distribution of neurons that can be inhomogeneous and vary with stimulation intensity and neuronal morphology across layers. We explore strategies to enhance stimulation precision, finding that optimizing stimulator optics may offer more significant improvements than preferentially somatic expression of the opsin through genetic targeting. Our results indicate that, with the right optical setup, single-photon optogenetics can precisely activate isolated neurons at the scale of functional cortical domains spanning several hundred micrometers.Significance Statement Sensory features, such as the position or orientation of a visual stimulus, are mapped onto the surface of cortex as functional domains. Their selective activation, that may enable eliciting complex percepts, is intensively pursued for basic science and clinical applications. However, delivery of light into one functional domain in optogenetically transfected cortex results in complex, widespread neuronal activity, spreading beyond the targeted domain. Our computational study reveals that neuron morphology contributes to this diffuse response in a cortical-layer and intensity-dependent manner. We show that enhancing the stimulator optics is more effective than soma-targeting of the opsin in increasing spatial precision of stimulation. Our simulations provide insights for designing optogenetic stimulation protocols and hardware to achieve selective activation of functional domains.

Towards imagined speech: Identification of brain states from EEG signals for BCI-based communication systems

BackgroundThe electroencephalogram (EEG) based brain-computer interface (BCI) system employing imagined speech serves as a mechanism for decoding EEG signals to facilitate control over external devices or communication with the external world at the moment the user desires. To effectively deploy such BCIs, it is imperative to accurately discern various brain states from continuous EEG signals when users initiate word imagination. New methodThis study involved the acquisition of EEG signals from 15 subjects engaged in four states: resting, listening, imagined speech, and actual speech, each involving a predefined set of 10 words. The EEG signals underwent preprocessing, segmentation, spatio-temporal and spectral analysis of each state, and functional connectivity analysis using the phase locking value (PLV) method. Subsequently, five features were extracted from the frequency and time-frequency domains. Classification tasks were performed using four machine learning algorithms in both pair-wise and multiclass scenarios, considering subject-dependent and subject-independent data. ResultsIn the subject-dependent scenario, the random forest (RF) classifier achieved a maximum accuracy of 94.60 % for pairwise classification, while the artificial neural network (ANN) classifier achieved a maximum accuracy of 66.92 % for multiclass classification. In the subject-independent scenario, the random forest (RF) classifier achieved maximum accuracies of 81.02 % for pairwise classification and 55.58 % for multiclass classification. Moreover, EEG signals were classified based on frequency bands and brain lobes, revealing that the theta (θ) and delta (δ) bands, as well as the frontal and temporal lobes, are sufficient for distinguishing between brain states. ConclusionThe findings promise to develop a system capable of automatically segmenting imagined speech segments from continuous EEG signals.

User-wise perturbations for user identity protection in EEG-based BCIs.

An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied more accurate BCIs, but much less attention has been paid to the ethics of BCIs. Aside from task-specific information, EEG signals also contain rich private information, e.g., user identity, emotion, disorders, etc., which should be protected. We show for the first time that adding user-wise perturbations can make identity information in EEG unlearnable. We propose four types of user-wise privacy-preserving perturbations, i.e., random noise, synthetic noise, error minimization noise, and error maximization noise. After adding the proposed perturbations to EEG training data, the user identity information in the data becomes unlearnable, while the BCI task information remains unaffected. Experiments on six EEG datasets using three neural network classifiers and various traditional machine learning models demonstrated the robustness and practicability of the proposed perturbations. Our research shows the feasibility of hiding user identity information in EEG data without impacting the primary BCI task information.

Brain and Muscle derived features to discriminate simple hand motor tasks for a rehabilitative BCI: comparative study on healthy and post-stroke individuals.

Brain-Computer Interfaces targeting post-stroke recovery of the upper limb employ mainly electroencephalography to decode movement-related brain activation. Recently hybrid systems including muscular activity were introduced. We compared the motor task discrimination abilities of three different features, namely event-related desynchronization/synchronization (ERD/ERS) and movement-related cortical potential (MRCP) as brain-derived features and cortico-muscular coherence (CMC) as a hybrid brain-muscle derived feature, elicited in 13 healthy subjects and 13 stroke patients during the execution/attempt of two simple hand motor tasks (finger extension and grasping) commonly employed in upper limb rehabilitation protocols. &#xD;Approach. We employed a three-way statistical design to investigate whether their ability to discriminate the two movements follows a specific temporal evolution along the movement execution and is eventually different among the three features and between the two groups. We also investigated the differences in performance at the single-subject level.&#xD;Main results. The ERD/ERS and the CMC-based classification showed similar temporal evolutions of the performance with a significant increase in accuracy during the execution phase while MRCP-based accuracy peaked at movement onset. Such temporal dynamics were similar but slower in stroke patients when the movements were attempted with the affected hand. Moreover, CMC outperformed the two brain features in healthy subjects and stroke patients when performing the task with their unaffected hand, whereas a higher variability across subjects was observed in patients performing the tasks with their affected hand. Interestingly, brain features performed better in this latter condition with respect to healthy subjects. &#xD;Significance. Our results provide hints to improve the design of Brain-Computer Interfaces for post-stroke rehabilitation, emphasizing the need for personalized approaches tailored to patients' characteristics and to the intended rehabilitative target.

Great Leaps Forward in Brain-Computer Interfaces for ALS Communication

Great Leaps Forward in Brain-Computer Interfaces for ALS Communication

Artificial intelligence for brain disease diagnosis using electroencephalogram signals.

Brain signals refer to electrical signals or metabolic changes that occur as a consequence of brain cell activity. Among the various non-invasive measurement methods, electroencephalogram (EEG) stands out as a widely employed technique, providing valuable insights into brain patterns. The deviations observed in EEG reading serve as indicators of abnormal brain activity, which is associated with neurological diseases. Brain‒computer interface (BCI) systems enable the direct extraction and transmission of information from the human brain, facilitating interaction with external devices. Notably, the emergence of artificial intelligence (AI) has had a profound impact on the enhancement of precision and accuracy in BCI technology, thereby broadening the scope of research in this field. AI techniques, encompassing machine learning (ML) and deep learning (DL) models, have demonstrated remarkable success in classifying and predicting various brain diseases. This comprehensive review investigates the application of AI in EEG-based brain disease diagnosis, highlighting advancements in AI algorithms.

Improvement of Error-Related Potential Monitoring in Brain–Computer Interface Based on Optimal Feature Dimensionality Selection

Improvement of Error-Related Potential Monitoring in Brain–Computer Interface Based on Optimal Feature Dimensionality Selection

HASTF: a hybrid attention spatio-temporal feature fusion network for EEG emotion recognition.

EEG-based emotion recognition has gradually become a new research direction, known as affective Brain-Computer Interface (aBCI), which has huge application potential in human-computer interaction and neuroscience. However, how to extract spatio-temporal fusion features from complex EEG signals and build learning method with high recognition accuracy and strong interpretability is still challenging. In this paper, we propose a hybrid attention spatio-temporal feature fusion network for EEG-based emotion recognition. First, we designed a spatial attention feature extractor capable of merging shallow and deep features to extract spatial information and adaptively select crucial features under different emotional states. Then, the temporal feature extractor based on the multi-head attention mechanism is integrated to perform spatio-temporal feature fusion to achieve emotion recognition. Finally, we visualize the extracted spatial attention features using feature maps, further analyzing key channels corresponding to different emotions and subjects. Our method outperforms the current state-of-the-art methods on two public datasets, SEED and DEAP. The recognition accuracy are 99.12% ± 1.25% (SEED), 98.93% ± 1.45% (DEAP-arousal), and 98.57% ± 2.60% (DEAP-valence). We also conduct ablation experiments, using statistical methods to analyze the impact of each module on the final result. The spatial attention features reveal that emotion-related neural patterns indeed exist, which is consistent with conclusions in the field of neurology. The experimental results show that our method can effectively extract and fuse spatial and temporal information. It has excellent recognition performance, and also possesses strong robustness, performing stably across different datasets and experimental environments for emotion recognition.