Electroencephalogram Based Brain Computer Interface Research Articles

Brain-computer interface in critical care and rehabilitation.

This comprehensive review explores the broad landscape of brain-computer interface (BCI) technology and its potential use in intensive care units (ICUs), particularly for patients with motor impairments such as quadriplegia or severe brain injury. By employing brain signals from various sensing techniques, BCIs offer enhanced communication and motor rehabilitation strategies for patients. This review underscores the concept and efficacy of noninvasive, electroencephalogram-based BCIs in facilitating both communicative interactions and motor function recovery. Additionally, it highlights the current research gap in intuitive "stop" mechanisms within motor rehabilitation protocols, emphasizing the need for advancements that prioritize patient safety and individualized responsiveness. Furthermore, it advocates for more focused research that considers the unique requirements of ICU environments to address the challenges arising from patient variability, fatigue, and limited applicability of current BCI systems outside of experimental settings.

Winning algorithms in BCI Controlled Robot Contest in World Robot Contest 2022: BCI Turing Test

The Turing Test is a method of testing whether a machine has human intelligence. A novel brain–computer interface (BCI) Turing Test was proposed in the BCI Controlled Robot Contest in World Robot Contest 2022. Contestants developed algorithms that can distinguish if an instruction is issued by a human. Participants collaborated with an electroencephalogram-based BCI to play a soccer game in a virtual scenario. Participants were asked to perform steady-state visual evoked potential (SSVEP) tasks or motor imagery (MI) tasks to control the robots or be in an idle state to mimic the system giving instructions on behalf of the participants. Several algorithms proposed in this competition are developed based on the concept that the idle state is a category in multiclass classification problems. This paper details the algorithms of the top five teams with the best performance in the final, lists the popular classification models and algorithms for MI and SSVEP, and discusses the effectiveness of each approach in improving classification performance and reducing the computation time.

Neurofeedback Training With an Electroencephalogram-Based Brain-Computer Interface Enhances Emotion Regulation

Neurofeedback Training With an Electroencephalogram-Based Brain-Computer Interface Enhances Emotion Regulation

Friend-guard adversarial noise designed for electroencephalogram-based brain–computer interface spellers

An electroencephalogram (EEG)–based brain–computer interface (BCI) speller is a system that conveys thought to enable communication between humans and computers using brain conduction. This system is useful for patients with severe disabilities and provides a way for them to communicate with computers or with other people. The EEG-based BCI speller system is being studied in various ways, but it is vulnerable to adversarial examples. An adversarial example is an attack sample created by adding a little noise to an original sample in such a way that it appears normal to humans but will be misclassified by the model. Adversarial examples can be useful in situations such as military scenarios in which there is a mixture of friendly models and enemy models. When a friendly BCI speller and an enemy BCI speller coexist, an adversarial example may be mistakenly taken by the enemy BCI speller to signal an incorrect intention on the part of the person with a disability; in another scenario, an enemy BCI speller may leak personal information of the individual with a disability or initiate unwanted financial transactions. In this paper, we propose a method to create such a sample, called a “friend-guard” EEG adversarial example. In the proposed method, a very small EEG signal is added to an original sample, creating an adversarial example that the friendly model will classify correctly but the enemy model will classify incorrectly. A P300 dataset was used in the experimental evaluation of the method, and linear regression models were used as target models. In the experiment, the proposed method was able to generate friend-guard EEG adversarial examples that were incorrectly classified with success rates of 88.4% and 69.7% by the enemy model for subject A and subject B, respectively, while maintaining the accuracy of the friendly model at 85.9% and 74.4% for subjects A and B, respectively.

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review.

Recently, a novel electroencephalogram-based brain-computer interface (EVE-BCI) using the vibrotactile stimulus shows great potential for an alternative to other typical motor imagery and visual-based ones. (i) Objective: in this review, crucial aspects of EVE-BCI are extracted from the literature to summarize its key factors, investigate the synthetic evidence of feasibility, and generate recommendations for further studies. (ii) Method: five major databases were searched for relevant publications. Multiple key concepts of EVE-BCI, including data collection, stimulation paradigm, vibrotactile control, EEG signal processing, and reported performance, were derived from each eligible article. We then analyzed these concepts to reach our objective. (iii) Results: (a) seventy-nine studies are eligible for inclusion; (b) EEG data are mostly collected among healthy people with an embodiment of EEG cap in EVE-BCI development; (c) P300 and Steady-State Somatosensory Evoked Potential are the two most popular paradigms; (d) only locations of vibration are heavily explored by previous researchers, while other vibrating factors draw little interest. (e) temporal features of EEG signal are usually extracted and used as the input to linear predictive models for EVE-BCI setup; (f) subject-dependent and offline evaluations remain popular assessments of EVE-BCI performance; (g) accuracies of EVE-BCI are significantly higher than chance levels among different populations. (iv) Significance: we summarize trends and gaps in the current EVE-BCI by identifying influential factors. A comprehensive overview of EVE-BCI can be quickly gained by reading this review. We also provide recommendations for the EVE-BCI design and formulate a checklist for a clear presentation of the research work. They are useful references for researchers to develop a more sophisticated and practical EVE-BCI in future studies.

Electroencephalogram based Brain Computer Interface System Analysis

Electroencephalogram is the study of electrical signals of the brain recorded using a mesh like structure containing electrodes that is placed on the scalp. The history of EEG dates more than a century back, when the brains of rabbits and monkeys were studied and almost 50 years later in the year 1924, the first ever brain activity of human was noted by famed psychiatrist and physiologist Hans Berger from Germany. EEG based systems that can communicate with the brain are categorized as Brain Computer Interface i.e. BCI. The electrodes read the brain signals, amplifies them in order to be studied more accurately by the machine send them to machine after converting it into digital form. With the recent changes in technology better electrodes are being used which can catch highly sensitive signals as well.[1][2] EEG based BCI systems can change the world for many people as it holds so much power if thought properly. This paper reviews the EEG functioning and some innovations in BCI and also proposes ideas about potential help for people suffering from hearing and speech impairment.

General Concepts on Electroencephalography-Based Brain-Computer Interface Systems

Brain-computer interface (BCI) technology primarily helps the physically challenged people get in touch with the outside world using the brain signals. Because of the simplicity of the electroencephalogram-based BCI methods, a number of researchers have shown interest and have done a lot of work in this field. However, for a common man, to understand the concept of BCI, the article presents a fundamental model with a brief summary of the various steps involved in the process of the whole system. Finally, some research work done to date has also been discussed. The whole article will help a person to understand the basic concepts of the electroencephalogram-based BCI system going through the model to the discussions.

Jaya Based ANFIS for Monitoring of Two Class Motor Imagery Task

The brain-computer interface (BCI) identifies brain patterns to translate thoughts into action. The identification relies on the performance of the classifier. In this paper, identification and monitoring of electroencephalogram-based BCI for motor imagery (MI) task is proposed by an efficient adaptive neuro-fuzzy classifier (NFC). The Jaya optimization algorithm is integrated with adaptive neuro-fuzzy inference systems to enhance classification accuracy. The linguistic hedge (LH) is used for proper elicitation and pruning of the fuzzy rules and network is trained using scaled conjugate gradient (SCG) and speeding up SCG (SSCG) techniques. In this paper, Jaya-based k-means is applied to divide the feature set into two mutually exclusive clusters and fire the fuzzy rule. The performance of the proposed classifier, Jaya-based NFC using SSCG as training algorithm and is powered by LH (JayaNFCSSCGLH), is compared with four different NFCs for classifying two class MI-based tasks. We observed a shortening of computation time per iteration by 57.78% in the case of SSCG as compared with the SCG technique of training. LH-based feature selecting capability of the proposed classifier not only reduces computation time but also improves the accuracy by discarding irrelevant features. Lesser computation time with fast convergence and high accuracy among considered NFCs make it a suitable choice for the real-time application. Supremacy of JayaNFCSSCGLH among the considered classifier is validated through Friedman test. Classification result is used to control switching of light emitting diode, turning thoughts into action.

Brain–Computer Interface for Neurorehabilitation of Upper Limb After Stroke

Current rehabilitation therapies for stroke rely on physical practice (PP) by the patients. Motor imagery (MI), the imagination of movements without physical action, presents an alternate neurorehabilitation for stroke patients without relying on residue movements. However, MI is an endogenous mental process that is not physically observable. Recently, advances in brain-computer interface (BCI) technology have enabled the objective detection of MI that spearheaded this alternate neurorehabilitation for stroke. In this review, we present two strategies of using BCI for neurorehabilitation after stroke: detecting MI to trigger a feedback, and detecting MI with a robot to provide concomitant MI and PP. We also present three randomized control trials that employed these two strategies for upper limb rehabilitation. A total of 125 chronic stroke patients were screened over six years. The BCI screening revealed that 103 (82%) patients can use electroencephalogram-based BCI, and 75 (60%) performed well with accuracies above 70%. A total of 67 patients were recruited to complete one of the three RCTs ranging from two to six weeks of which 26 patients, who underwent BCI neurorehabilitation that employed these two strategies, had significant motor improvement of 4.5 measured by Fugl-Meyer Motor Assessment of the upper extremity. Hence, the results demonstrate clinical efficacy of using BCI as an alternate neurorehabilitation for stroke.

Sensorimotor Modulation Assessment and Brain-Computer Interface Training in Disorders of Consciousness

ObjectivesTo assess awareness in subjects who are in a minimally conscious state by using an electroencephalogram-based brain-computer interface (BCI), and to determine whether these patients may learn to modulate sensorimotor rhythms with visual feedback, stereo auditory feedback, or both. DesignInitial assessment included imagined hand movement or toe wiggling to activate sensorimotor areas and modulate brain rhythms in 90 trials (4 subjects). Within-subject and within-group analyses were performed to evaluate significant activations. A within-subject analysis was performed involving multiple BCI technology training sessions to improve the capacity of the user to modulate sensorimotor rhythms through visual and auditory feedback. SettingHospital, homes of subjects, and a primary care facility. ParticipantsSubjects (N=4; 3 men, 1 woman) who were in a minimally conscious state (age range, 27–53y; 1–12y after brain injury). InterventionsNot applicable. Main Outcome MeasuresAwareness detection was determined from sensorimotor patterns that differed for each motor imagery task. BCI performance was determined from the mean classification accuracy of brain patterns by using a BCI signal processing framework and assessment of performance in multiple sessions. ResultsAll subjects demonstrated significant and appropriate brain activation during the initial assessment, and real-time feedback was provided to improve arousal. Consistent activation was observed in multiple sessions. ConclusionsThe electroencephalogram-based assessment showed that patients in a minimally conscious state may have the capacity to operate a simple BCI-based communication system, even without any detectable volitional control of movement.

Discriminative Common Spatial Pattern Sub-bands Weighting Based on Distinction Sensitive Learning Vector Quantization Method in Motor Imagery Based Brain-computer Interface.

Common spatial pattern (CSP) is a method commonly used to enhance the effects of event-related desynchronization and event-related synchronization present in multichannel electroencephalogram-based brain-computer interface (BCI) systems. In the present study, a novel CSP sub-band feature selection has been proposed based on the discriminative information of the features. Besides, a distinction sensitive learning vector quantization based weighting of the selected features has been considered. Finally, after the classification of the weighted features using a support vector machine classifier, the performance of the suggested method has been compared with the existing methods based on frequency band selection, on the same BCI competitions datasets. The results show that the proposed method yields superior results on "ay" subject dataset compared against existing approaches such as sub-band CSP, filter bank CSP (FBCSP), discriminative FBCSP, and sliding window discriminative CSP.

Optimizing spatial filters by minimizing within-class dissimilarities in electroencephalogram-based brain-computer interface.

A major challenge in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the inherent nonstationarities in the EEG data. Variations of the signal properties from intra and inter sessions often lead to deteriorated BCI performances, as features extracted by methods such as common spatial patterns (CSP) are not invariant against the changes. To extract features that are robust and invariant, this paper proposes a novel spatial filtering algorithm called Kullback-Leibler (KL) CSP. The CSP algorithm only considers the discrimination between the means of the classes, but does not consider within-class scatters information. In contrast, the proposed KLCSP algorithm simultaneously maximizes the discrimination between the class means, and minimizes the within-class dissimilarities measured by a loss function based on the KL divergence. The performance of the proposed KLCSP algorithm is compared against two existing algorithms, CSP and stationary CSP (sCSP), using the publicly available BCI competition III dataset IVa and a large dataset from stroke patients performing neuro-rehabilitation. The results show that the proposed KLCSP algorithm significantly outperforms both the CSP and the sCSP algorithms, in terms of classification accuracy, by reducing within-class variations. This results in more compact and separable features.

Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: A feasibility study

BackgroundWe developed an electroencephalogram-based brain computer interface system to modulate functional electrical stimulation (FES) to the affected tibialis anterior muscle in a stroke patient. The intensity of FES current increased in a stepwise manner when the event-related desynchronization (ERD) reflecting motor intent was continuously detected from the primary cortical motor area.MethodsWe tested the feasibility of the ERD-modulated FES system in comparison with FES without ERD modulation. The stroke patient who presented with severe hemiparesis attempted to perform dorsiflexion of the paralyzed ankle during which FES was applied either with or without ERD modulation.ResultsAfter 20 minutes of training, the range of movement at the ankle joint and the electromyography amplitude of the affected tibialis anterior muscle were significantly increased following the ERD-modulated FES compared with the FES alone.ConclusionsThe proposed rehabilitation technique using ERD-modulated FES for stroke patients was feasible. The system holds potentials to improve the limb function and to benefit stroke patients.

52. Modulation of event related desynchronization during motor imagery with transcranial direct current stimulation (tDCS) in patients with chronic hemiparetic stroke

Electroencephalogram-based brain–computer interface (BCI) has been developed as a new neurorehabilitative tool for patients with severe hemiparesis. However, its application has been limited because of difficulty detecting stable brain signals from the affected hemisphere. It has been reported that transcranial direct current stimulation (tDCS) can modulate event-related desynchronization (ERD) in healthy persons. The objective of this study was to test the hypothesis that anodal tDCS could modulate ERD in patients with severe hemiparetic stroke. The participants were six patients with chronic hemiparetic stroke (mean age, 56.8 ± 9.5 years; mean time from the onset, 70.0 ± 19.6 months; Fugl-Meyer Assessment upper extremity motor score, 30.8 ± 16.5). We applied anodal tDCS (10 min, 1 mA) and sham stimulation over the affected primary motor cortex in a random order. ERD of the mu rhythm (mu ERD) with motor imagery of extension of the affected finger was assessed before and after anodal tDCS and sham stimulation. Mu ERD of the affected hemisphere increased significantly after anodal tDCS, whereas it did not change after sham stimulation. Our results show that anodal tDCS can increase mu ERD in patients with hemiparetic stroke, indicating that anodal tDCS could be used as a conditioning tool for BCI in stroke patients.

Modulation of event-related desynchronization during motor imagery with transcranial direct current stimulation (tDCS) in patients with chronic hemiparetic stroke

Electroencephalogram-based brain-computer interface (BCI) has been developed as a new neurorehabilitative tool for patients with severe hemiparesis. However, its application has been limited because of difficulty detecting stable brain signals from the affected hemisphere. It has been reported that transcranial direct current stimulation (tDCS) can modulate event-related desynchronization (ERD) in healthy persons. The objective of this study was to test the hypothesis that anodal tDCS could modulate ERD in patients with severe hemiparetic stroke. The participants were six patients with chronic hemiparetic stroke (mean age, 56.8±9.5years; mean time from the onset, 70.0±19.6months; Fugl-Meyer Assessment upper extremity motor score, 30.8±16.5). We applied anodal tDCS (10min, 1mA) and sham stimulation over the affected primary motor cortex in a random order. ERD of the mu rhythm (mu ERD) with motor imagery of extension of the affected finger was assessed before and after anodal tDCS and sham stimulation. Mu ERD of the affected hemisphere increased significantly after anodal tDCS, whereas it did not change after sham stimulation. Our results show that anodal tDCS can increase mu ERD in patients with hemiparetic stroke, indicating that anodal tDCS could be used as a conditioning tool for BCI in stroke patients.

An online brain–computer interface based on shifting attention to concurrent streams of auditory stimuli

We report on the development and online testing of an electroencephalogram-based brain–computer interface (BCI) that aims to be usable by completely paralysed users—for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects' modulation of N1 and P3 ERP components measured during single 5 s stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare ‘oddball’ stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject's attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology.

A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces

Event-related desynchronization/synchronization patterns during right/left motor imagery (MI) are effective features for an electroencephalogram-based brain-computer interface (BCI). As MI tasks are subject-specific, selection of subject-specific discriminative frequency components play a vital role in distinguishing these patterns. This paper proposes a new discriminative filter bank (FB) common spatial pattern algorithm to extract subject-specific FB for MI classification. The proposed method enhances the classification accuracy in BCI competition III dataset IVa and competition IV dataset IIb. Compared to the performance offered by the existing FB-based method, the proposed algorithm offers error rate reductions of 17.42% and 8.9% for BCI competition datasets III and IV, respectively.

Electro-encephalogram based brain–computer interface: improved performance by mental practice and concentration skills

Mental imagination is the essential part of the most EEG-based communication systems. Thus, the quality of mental rehearsal, the degree of imagined effort, and mind controllability should have a major effect on the performance of electro-encephalogram (EEG) based brain-computer interface (BCI). It is now well established that mental practice using motor imagery improves motor skills. The effects of mental practice on motor skill learning are the result of practice on central motor programming. According to this view, it seems logical that mental practice should modify the neuronal activity in the primary sensorimotor areas and consequently change the performance of EEG-based BCI. For developing a practical BCI system, recognizing the resting state with eyes opened and the imagined voluntary movement is important. For this purpose, the mind should be able to focus on a single goal for a period of time, without deviation to another context. In this work, we are going to examine the role of mental practice and concentration skills on the EEG control during imaginative hand movements. The results show that the mental practice and concentration can generally improve the classification accuracy of the EEG patterns. It is found that mental training has a significant effect on the classification accuracy over the primary motor cortex and frontal area.

Answering questions with an electroencephalogram-based brain-computer interface

Objective: To demonstrate that humans can learn to control selected electroencephalographic components and use that control to answer simple questions. Methods: Four adults (one with amyotrophic lateral sclerosis) learned to use electroencephalogram (EEG) mu rhythm (8 to 12Hz) or beta rhythm (18 to 25Hz) activity over sensorimotor cortex to control vertical cursor movement to targets at the top or bottom edge of a video screen. In subsequent sessions, the targets were replaced with the words YES and NO, and individuals used the cursor to answer spoken YES/NO questions from single- or multiple-topic question sets. They confirmed their answers through the response verification (RV) procedure, in which the word positions were switched and the question was answered again. Results: For 5 consecutive sessions after initial question training, individuals were asked an average of 4.0 to 4.6 questions per minute; 64% to 87% of their answers were confirmed by the RV procedure and 93% to 99% of these answers were correct. Performances for single- and multipletopic question sets did not differ significantly. Conclusions: The results indicate that (1) EEG-based cursor control can be used to answer simple questions with a high degree of accuracy, (2) attention to auditory queries and formulation of answers does not interfere with EEG-based cursor control, (3) question complexity (at least as represented by single versus multiple-topic question sets) does not noticeably affect performance, and (4) the RV procedure improves accuracy as expected. Several options for increasing the speed of communication appear promising. An EEG-based brain-computer interface could provide a new communication and control modality for people with severe motor disabilities.