Current Brain-computer Interface Research Articles

Chinese expert consensus on implementation and management of brain-computer interface clinical research in neurological diseases

The development of brain-computer interface (BCI) technology and its preliminary research results show great clinical application prospects. In particular, the application of BCI technologyis a hot research topic in the field of nervous system diseases at present, but the current BCI technology is still in the stage of experimental exploration, needing systematic and standardized clinical research validation. For this purpose, the consensus is led by the Society of Neurosurgery of Chinese Medical Association and Society of Cerebrovascular Surgery of Chinese Stroke Association. Based on the in-depth discussion of multidisciplinary experts and the vote of the Delphi Method, the guidelines and principles are proposed for pre-clinical qualification review, clinical research implementation and management, and long-term effect tracking and evaluation, so as to standardize research ethics and clinical research procedures and further promote the extensive application and in-depth development of BCI technology in the treatment of nervous system diseases.

NeuronLab: BCI framework for the study of biosignals

Brain–Computer Interfaces (BCIs) allow the acquisition of brain activity using non-invasive techniques such as Electroencephalography (EEG). Since BCI devices do not commonly interpret the acquired EEG signals, external software applications play a critical role in the BCI lifecycle. Despite the literature offering a great variety of platforms and frameworks, they present several limitations, such as implementing old software architectures or needing more functionality to cover all phases of the BCI lifecycle. Based on these limitations, this work proposes the design and implementation of NeuronLab, a secure, multi-platform, standalone, multi-paradigm, and web-based framework that defines all BCI lifecycle phases and provides novel functionality compared to current open-source BCI software, such as sharing experiments between researchers and storing data on the cloud. This framework has been validated in two experiments common in BCI literature: P300 identification and limb movements detection. This verification has been performed based on performance metrics, such as CPU and RAM consumption, highlighting that NeuronLab is a promising solution for BCI scenarios requiring a distributed and collaborative platform for researchers and practitioners.

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

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.

Resting state EEG assisted imagined vowel phonemes recognition by native and non-native speakers using brain connectivity measures.

Communication is challenging for disabled individuals, but with advancement of brain-computer interface (BCI) systems, alternative communication systems can be developed. Current BCI spellers, such as P300, SSVEP, and MI, have drawbacks like reliance on external stimuli or conversation irrelevant mental tasks. In contrast to these systems, Imagined speech based BCI systems rely on directly decoding the vowels/words user is thinking, making them more intuitive, user friendly and highly popular among Brain-Computer-Interface (BCI) researchers. However, more research needs to be conducted on how subject-specific characteristics such as mental state, age, handedness, nativeness and resting state activity affects the brain's output during imagined speech. In an overt speech, it is evident that native and non-native speakers' brains function differently. Therefore, this paper explores how nativeness to language affects EEG signals while imagining vowel phonemes, using brain-map analysis and scalogram and also investigates the inclusion of features extracted from resting state EEG with imagined state EEG. The Fourteen-channel EEG for Imagined Speech (FEIS) dataset was used to analyse the EEG signals recorded while imagining vowel phonemes for 16 subjects (nine native English and seven non-native Chinese). For the classification of vowel phonemes, different connectivity measures such as covariance, coherence, and Phase Synchronous Index-PSI were extracted and analysed using statistics based Multivariate Analysis of Variance (MANOVA) approach. Different fusion strategies (difference, concatenation, Common Spatial Pattern-CSP and Canonical Correlation Analysis-CCA) were carried out to incorporate resting state EEG connectivity measures with imagined state connectivity measures for enhancing the accuracy of imagined vowel phoneme recognition. Simulation results revealed that concatenating imagined state and rest state covariance and PSI features provided the maximum accuracy of 92.78% for native speakers and 94.07% for non-native speakers.

Evaluation of an online SSVEP-BCI with fast system setup

The brain–computer interface (BCI) plays an important role in neural restoration. Current BCI systems generally require complex experimental preparation to perform well, but this time-consuming process may hinder their use in clinical applications. To explore the feasibility of simplifying the BCI system setup, a wearable BCI system based on the steady-state visual evoked potential (SSVEP) was developed and evaluated. Fifteen healthy participants were recruited to test the fast-setup system using dry and wet electrodes in a real-life scenario. In this study, the average system setup time for the dry electrode was 38.40 seconds and that for the wet electrode was 103.40 seconds, which are times appreciably shorter than those in previous BCI experiments, enabling a rapid setup of the BCI system. Although the electroencephalogram (EEG) signal quality was low in this fast-setup BCI experiment, the BCI system achieved an information transfer rate of 138.89 bits/min with an eight-channel wet electrode and an information transfer rate of 70.59 bits/min with an eight-channel dry electrode, showing that the overall performance was close to that in traditional experiments. In addition, the results suggest that the solutions of a multi-channel dry electrode or few-channel wet electrode may be suitable for the fast-setup SSEVP-BCI. This fast-setup SSVEP-BCI has the advantages of simple preparation and stable performance and is thus conducive to promoting the use of the BCI in clinical practice.

Transfer learning with data alignment and optimal transport for EEG based motor imagery classification

Objective. The non-stationarity of electroencephalogram (EEG) signals and the variability among different subjects present significant challenges in current Brain–Computer Interfaces (BCI) research, which requires a time-consuming specific calibration procedure to address. Transfer Learning (TL) offers a potential solution by leveraging data or models from one or more source domains to facilitate learning in the target domain, so as to address these challenges. Approach. In this paper, a novel Multi-source domain Transfer Learning Fusion (MTLF) framework is proposed to address the calibration problem. Firstly, the method transforms the source domain data with the resting state segment data, in order to decrease the differences between the source domain and the target domain. Subsequently, feature extraction is performed using common spatial pattern. Finally, an improved TL classifier is employed to classify the target samples. Notably, this method does not require the label information of target domain samples, while concurrently reducing the calibration workload. Main results. The proposed MTLF is assessed on Datasets 2a and 2b from the BCI Competition IV. Compared with other algorithms, our method performed relatively the best and achieved mean classification accuracy of 73.69% and 70.83% on Datasets 2a and 2b respectively. Significance. Experimental results demonstrate that the MTLF framework effectively reduces the discrepancy between the source and target domains and acquires better classification performance on two motor imagery datasets.

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.

The Performance of a Lip-Sync Imagery Model, New Combinations of Signals, a Supplemental Bond Graph Classifier, and Deep Formula Detection as an Extraction and Root Classifier for Electroencephalograms and Brain–Computer Interfaces

Many current brain–computer interface (BCI) applications depend on the quick processing of brain signals. Most researchers strive to create new methods for future implementation and enhance existing models to discover an optimal feature set that can operate independently. This study focuses on four key concepts that will be used to complete future works. The first concept is related to potential future communication models, whereas the others aim to enhance previous models or methodologies. The four concepts are as follows. First, we suggest a new communication imagery model as a substitute for a speech imager that relies on a mental task approach. As speech imagery is intricate, one cannot imagine the sounds of every character in every language. Our study proposes a new mental task model for lip-sync imagery that can be employed in all languages. Any character in any language can be used with this mental task model. In this study, we utilized two lip-sync movements to indicate two sounds, characters, or letters. Second, we considered innovative hybrid signals. Choosing an unsuitable frequency range can lead to ineffective feature extractions. Therefore, the selection of an appropriate frequency range is crucial for processing. The ultimate goal of this method is to accurately discover distinct frequencies of brain imagery activities. The restricted frequency range combination presents an initial proposal for generating fragmented, continuous frequencies. The first model assesses two 4 Hz intervals as filter banks. The primary objective is to discover new combinations of signals at 8 Hz by selecting filter banks with a 4 Hz scale from the frequency range of 4 Hz to 40 Hz. This approach facilitates the acquisition of efficient and clearly defined features by reducing similar patterns and enhancing distinctive patterns of brain activity. Third, we introduce a new linear bond graph classifier as a supplement to a linear support vector machine (SVM) when handling noisy data. The performance of the linear support vector machine (SVM) significantly declines under high-noise conditions. To complement the linear support vector machine (SVM) in noisy-data situations, we introduce a new linear bond graph classifier. Fourth, this paper presents a deep-learning model for formula recognition that converts the first-layer data into a formula extraction model. The primary goal is to decrease the noise in the formula coefficients of the subsequent layers. The output of the final layer comprises coefficients chosen by different functions at various levels. The classifier then extracts the root interval for each formula, and a diagnosis is established based on these intervals. The final goal of the last idea is to explain the main brain imagery activity formula using a combination formula for similar and distinctive brain imagery activities. The results of implementing all of the proposed methods are reported. The results range between 55% and 98%. The lowest result is 55% for the deep detection formula, and the highest result is 98% for new combinations of signals.

BCI Control of a Robotic arm based on SSVEP with Moving Stimuli for Reach and grasp Tasks.

Brain-computer interface (BCI) provides a novel technology for patients and healthy human subjects to control a robotic arm. Currently, BCI control of a robotic arm to complete the reaching and grasping tasks in an unstructured environment is still challenging because the current BCI technology does not meet the requirement of manipulating a multi-degree robotic arm accurately and robustly. BCI based on steady-state visual evoked potential (SSVEP) could output a high information transfer rate; however, the conventional SSVEP paradigm failed to control a robotic arm to move continuously and accurately because the users have to switch their gaze between the flickering stimuli and the target frequently. This study proposed a novel SSVEP paradigm in which the flickering stimuli were attached to the robotic arm's gripper and moved with it. First, an offline experiment was designed to investigate the effects of moving flickering stimuli on the SSVEP's responses and decoding accuracy. After that, contrast experiments were conducted, and twelve subjects were recruited to participate in a robotic arm control experiment using both the paradigm one (P1, with moving flickering stimuli) and the paradigm two (P2, conventional fixed flickering stimuli) using a block randomization design to balance their sequences. Double blinks were used to trigger the grasping action asynchronously whenever the subjects were confident that the position of the robotic arm's gripper was accurate enough. Experimental results showed that the paradigm P1 with moving flickering stimuli provided a much better control performance than the conventional paradigm P2 in completing a reaching and grasping task in an unstructured environment. Subjects' subjective feedback scored by a NASA-TLX mental workload scale also corroborated the BCI control performance. The results of this study suggest that the proposed control interface based on SSVEP BCI provides a better solution for robotic arm control to complete the accurate reaching and grasping tasks.

Intracortical brain-computer interfaces in primates: a review and outlook.

Brain-computer interfaces (BCI) translate brain signals into artificial output to restore or replace natural central nervous system (CNS) functions. Multiple processes, including sensorimotor integration, decision-making, motor planning, execution, and updating, are involved in any movement. For example, a BCI may be better able to restore naturalistic motor behaviors if it uses signals from multiple brain areas and decodes natural behaviors' cognitive and motor aspects. This review provides an overview of the preliminary information necessary to plan a BCI project focusing on intracortical implants in primates. Since the brain structure and areas of non-human primates (NHP) are similar to humans, exploring the result of NHP studies will eventually benefit human BCI studies. The different types of BCI systems based on the target cortical area, types of signals, and decoding methods will be discussed. In addition, various successful state-of-the-art cases will be reviewed in more detail, focusing on the general algorithm followed in the real-time system. Finally, an outlook for improving the current BCI research studies will be debated.

Stylized faces enhance ERP features used for the detection of emotional responses.

For their ease of accessibility and low cost, current Brain-Computer Interfaces (BCI) used to detect subjective emotional and affective states rely largely on electroencephalographic (EEG) signals. Public datasets are available for researchers to design models for affect detection from EEG. However, few designs focus on optimally exploiting the nature of the stimulus elicitation to improve accuracy. The RSVP protocol is used in this experiment to present human faces of emotion to 28 participants while EEG was measured. We found that artificially enhanced human faces with exaggerated, cartoonish visual features significantly improve some commonly used neural correlates of emotion as measured by event-related potentials (ERPs). These images elicit an enhanced N170 component, well known to relate to the facial visual encoding process. Our findings suggest that the study of emotion elicitation could exploit consistent, high detail, AI generated stimuli transformations to study the characteristics of electrical brain activity related to visual affective stimuli. Furthermore, this specific result might be useful in the context of affective BCI design, where a higher accuracy in affect decoding from EEG can improve the experience of a user.

A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI.

Brain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals. This paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement. A classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%. This approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery.

Multivariate EEG Signal Processing Techniques for the Aid of Severely Disabled People

Electroencephalography (EEG) has been used for several years as a trace of signals for facilitating subjects with serious infirmities to communicate with computers and other devices. Many studies have revealed the correlation of mental tasks with the EEG signals for actual or fictional movements. However, the performance of Brain Computer Interface (BCI) using EEG signal is still below enough to assist any disabled people. One reason could be that the researchers in this field (motor imagery based BCI) normally use two to three channels of EEG signal. This might limit the performance of BCI, as an extra source of information generally helps in detecting a person's motor movement intentions. Therefore, the proposed research work is involved with three or more channels of EEG signal for online BCI. Two fundamental objectives for BCI based on motor movement imagery from multichannel signals are aimed at in this research work: i) to develop a technique of multivariate feature extraction for motor imagery related to multichannel EEG signals; and ii) to develop an appropriate machine learning based feature classification algorithm for Brain Computer Interface. Nevertheless, all other problems like interfacing and real-time operations with current BCIs are also addressed and attempts are made to reduce the problems. The methodology can be described by following steps as follows: i) at least 3 channels of EEG signal are recorded; ii) a few features are extracted from preprocessed EEG signal; iii) all extracted features are classified to generate commands for BCI; iv) finally evaluate the performance of the proposed algorithm for BCI. The challenge of this research work is to investigate and find an appropriate model for online (real-time) BCI with a realistic performance to be made in achieving better lives for people with severe disabilities in Malaysia and abroad.

Riemannian Channel Selection for BCI With Between-Session Non-Stationarity Reduction Capabilities.

Between-session non-stationarity is a major challenge of current Brain-Computer Interfaces (BCIs) that affects system performance. In this paper, we investigate the use of channel selection for reducing between-session non-stationarity with Riemannian BCI classifiers. We use the Riemannian geometry framework of covariance matrices due to its robustness and promising performances. Current Riemannian channel selection methods do not consider between-session non-stationarity and are usually tested on a single session. Here, we propose a new channel selection approach that specifically considers non-stationarity effects and is assessed on multi-session BCI data sets. We remove the least significant channels using a sequential floating backward selection search strategy. Our contributions include: 1) quantifying the non-stationarity effects on brain activity in multi-class problems by different criteria in a Riemannian framework and 2) a method to predict whether BCI performance can improve using channel selection. We evaluate the proposed approaches on three multi-session and multi-class mental tasks (MT)-based BCI datasets. They could lead to significant improvements in performance as compared to using all channels for datasets affected by between-session non-stationarity and to significant superiority to the state-of-the-art Riemannian channel selection methods over all datasets, notably when selecting small channel set sizes. Reducing non-stationarity by channel selection could significantly improve Riemannian BCI classification accuracy. Our proposed channel selection approach contributes to make Riemannian BCI classifiers more robust to between-session non-stationarities.

Clinical Brain–Computer Interface Challenge 2020 (CBCIC at WCCI2020): Overview, Methods and Results

In the field of brain-computer interface (BCI) research, the availability of high-quality open-access datasets is essential to benchmark the performance of emerging algorithms. The existing open-access datasets from past competitions mostly deal with healthy individuals' data, while the major application area of BCI is in the clinical domain. Thus the newly proposed algorithms to enhance the performance of BCI technology are very often tested against the healthy subjects' datasets only, which doesn't guarantee their success on patients' datasets which are more challenging due to the presence of more nonstationarity and altered neurodynamics. In order to partially mitigate this scarcity, Clinical BCI Challenge aimed to provide an open-access rich dataset of stroke patients recorded similar to a neurorehabilitation paradigm. Another key feature of this challenge is that unlike many competitions in the past, it was designed for algorithms in both with-in subject and cross-subject categories as a major thrust area of current BCI technology is to realize calibration-free BCI designs. In this paper, we have discussed the winning algorithms and their performances across both competition categories which may help develop advanced algorithms for reliable BCIs for real-world practical applications.

Online detection of movement during natural and self-initiated reach-and-grasp actions from EEG signals

Movement intention detection using electroencephalography (EEG) is a challenging but essential component of brain–computer interfaces (BCIs) for people with motor disabilities. Objective. The goal of this study is to develop a new experimental paradigm to perform asynchronous online detection of movement based on low-frequency time-domain EEG features, concretely on movement-related cortical potentials. The paradigm must be easily transferable to people without any residual upper-limb movement function and the BCI must be independent of upper-limb movement onset measurements and external cues. Approach. In a study with non-disabled participants, we evaluated a novel BCI paradigm to detect self-initiated reach-and-grasp movements. Two experimental conditions were involved. In one condition, participants performed reach-and-grasp movements to a target and simultaneously shifted their gaze towards it. In a control condition, participants solely shifted their gaze towards the target (oculomotor task). The participants freely decided when to initiate the tasks. After eye artefact correction, the EEG signals were time-locked to the saccade onset and the resulting amplitude features were exploited on a hierarchical classification approach to detect movement asynchronously. Main results. With regards to BCI performance, 54.1% (14.4% SD) of the movements were correctly identified, and all participants achieved a performance above chance-level (around 12%). An average of 21.5% (14.1% SD) of the oculomotor tasks were falsely detected as upper-limb movement. In an additional rest condition, 1.7 (1.6 SD) false positives per minute were measured. Through source imaging, movement information was mapped to sensorimotor, posterior parietal and occipital areas. Significance. We present a novel approach for movement detection using EEG signals which does not rely on upper-limb movement onset measurements or on the presentation of external cues. The participants’ behaviour closely matches the natural behaviour during goal-directed reach-and-grasp movements, which also constitutes an advantage with respect to current BCI protocols.

A Personalized Feature Extraction and Classification Method for Motor Imagery Recognition

In practical applications of the motor imagery-based brain–computer interface (BCI) system, the differences in electroencephalogram (EEG) signal manifestation and corresponding rhythm ranges in different individuals pose a significant challenge. The corresponding EEG features in different frequency bands differ; therefore, personalized screening must be conducted to obtain information that is conducive to the classification of EEG signals for different motor imageries. Also, in current BCI system, to obtain more information, multi-channel electrodes are often used to collect EEG signals, but also increasing the complexity of calculation. In this paper, a personalized feature extraction method based on filter bank and elastic net and a personalized channel selection based on Deep Belief Network to obtain a classification accuracy similar to or even higher than using all channels is proposed. Compared with the typically used feature extraction and classification algorithms, this method obtains higher calculation rates and recognition accuracy and provides a theoretical reference for the practical application of BCI systems. The shortcomings of the common spatial pattern (CSP) algorithm are addressed. The major contribution of this paper is the flexible screening of feature vectors and channels containing more classification information based on individual differences, thereby preventing the manual adjustment of specific frequency ranges in traditional CSP for performing feature extraction and avoiding inputting all channels. In the case study, the highest test accuracy reaches 86.94%.

Towards a mechanistic approach for the development of non-invasive brain-computer interfaces for motor rehabilitation.

Brain-computer interfaces (BCIs) designed for motor rehabilitation use brain signals associated with motor-processing states to guide neuroplastic changes in a state-dependent manner. These technologies are uniquely positioned to induce targeted and functionally relevant plastic changes in the human motor nervous system. However, while several studies have shown that BCI-based neuromodulation interventions may improve motor function in patients with lesions in the central nervous system, the neurophysiological structures and processes targeted with the BCI interventions have not been identified. In this review, we first summarize current knowledge of the changes in the central nervous system associated with learning new motor skills. Then, we propose a classification of current BCI paradigms for plasticity induction and motor rehabilitation based on the expected neural plastic changes promoted. This classification proposes four paradigms based on two criteria: the plasticity induction methods and the brain states targeted. The existing evidence regarding the brain circuits and processes targeted with these different BCIs is discussed in detail. The proposed classification aims to serve as a starting point for future studies trying to elucidate the underlying plastic changes following BCI interventions.

Detection of the Intention of Direction Changes During Gait Through EEG Signals.

Brain-Computer Interfaces (BCIs) are becoming an important technological tool for the rehabilitation process of patients with locomotor problems, due to their ability to recover the connection between brain and limbs by promoting neural plasticity. They can be used as assistive devices to improve the mobility of handicapped people. For this reason, current BCIs have to be improved to allow an accurate and natural use of external devices. This work proposes a novel methodology for the detection of the intention to change the direction during gait based on event-related desynchronization (ERD). Frequency and temporal features of the electroencephalographic (EEG) signals are characterized. Then, a selection of the most influential features and electrodes to differentiate the direction change intention from the walking is carried out. Best results are obtained when combining frequency and temporal features with an average accuracy of [Formula: see text]%, which are promising to be applied for future BCIs.

EEG Channel Selection Using A Modified Grey Wolf Optimizer

Consider an increasingly growing field of research, Brain-Computer Interface (BCI) is to form a direct channel of communication between a computer and the brain. However, extracting features of random time-varying EEG signals and their classification is a major challenge that faces current BCI. This paper proposes a modified grey wolf optimizer (MGWO) that can select optimal EEG channels to be used in (BCIs), the way that identifies main features and the immaterial ones from that dataset and the complexity to be removed. This allows (MGWO) to opt for optimal EEG channels as well as helping machine learning classification in its tasks when doing training to the classifier with the dataset. (MGWO), which imitates the grey wolves leadership and hunting manner nature and which consider metaheuristics swarm intelligence algorithms, is an integration with two modification to achieve the balance between exploration and exploitation the first modification applies exponential change for the number of iterations to increase search space accordingly exploitation, the second modification is the crossover operation that is used to increase the diversity of the population and enhance exploitation capability. Experimental results use four different EEG datasets BCI Competition IV- dataset 2a, BCI Competition IV- data set III, BCI Competition II data set III, and EEG Eye State from UCI Machine Learning Repository to evaluate the quality and effectiveness of the (MGWO). A cross-validation method is used to measure the stability of the (MGWO).