EEG-based BCIs Research Articles

Unsupervised heterogeneous domain adaptation for EEG classification

Objective. Domain adaptation has been recognized as a potent solution to the challenge of limited training data for electroencephalography (EEG) classification tasks. Existing studies primarily focus on homogeneous environments, however, the heterogeneous properties of EEG data arising from device diversity cannot be overlooked. This motivates the development of heterogeneous domain adaptation methods that can fully exploit the knowledge from an auxiliary heterogeneous domain for EEG classification. Approach. In this article, we propose a novel model named informative representation fusion (IRF) to tackle the problem of unsupervised heterogeneous domain adaptation in the context of EEG data. In IRF, we consider different perspectives of data, i.e. independent identically distributed (iid) and non-iid, to learn different representations. Specifically, from the non-iid perspective, IRF models high-order correlations among data by hypergraphs and develops hypergraph encoders to obtain data representations of each domain. From the non-iid perspective, by applying multi-layer perceptron networks to the source and target domain data, we achieve another type of representation for both domains. Subsequently, an attention mechanism is used to fuse these two types of representations to yield informative features. To learn transferable representations, the maximum mean discrepancy is utilized to align the distributions of the source and target domains based on the fused features. Main results. Experimental results on several real-world datasets demonstrate the effectiveness of the proposed model. Significance. This article handles an EEG classification situation where the source and target EEG data lie in different spaces, and what’s more, under an unsupervised learning setting. This situation is practical in the real world but barely studied in the literature. The proposed model achieves high classification accuracy, and this study is important for the commercial applications of EEG-based BCIs.

Personalized Classifier Selection for EEG-Based BCIs

Personalized Classifier Selection for EEG-Based BCIs

EEG-based finger movement classification with intrinsic time-scale decomposition.

Brain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five finger movements by neglecting the brain's idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals. In this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not. As a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems. When compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT).

Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications

Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications

Exploration of Person-Independent BCIs for Internal and External Attention-Detection in Augmented Reality

Adding attention-awareness to an Augmented Reality setting by using a Brain-Computer Interface promises many interesting new applications and improved usability. The possibly complicated setup and relatively long training period of EEG-based BCIs however, reduce this positive effect immensely. In this study, we aim at finding solutions for person-independent, training-free BCI integration into AR to classify internally and externally directed attention. We assessed several different classifier settings on a dataset of 14 participants consisting of simultaneously recorded EEG and eye tracking data. For this, we compared the classification accuracies of a linear algorithm, a non-linear algorithm, and a neural net that were trained on a specifically generated feature set, as well as a shallow neural net for raw EEG data. With a real-time system in mind, we also tested different window lengths of the data aiming at the best payoff between short window length and high classification accuracy. Our results showed that the shallow neural net based on 4-second raw EEG data windows was best suited for real-time person-independent classification. The accuracy for the binary classification of internal and external attention periods reached up to 88% accuracy with a model that was trained on a set of selected participants. On average, the person-independent classification rate reached 60%. Overall, the high individual differences could be seen in the results. In the future, further datasets are necessary to compare these results before optimizing a real-time person-independent attention classifier for AR.

An improved version of local activities estimation to enhance motor imagery classification

ObjectiveThe common spatial pattern (CSP) and its variants are popular in the EEG-based motor imagery BCIs. However, this method has some drawbacks, especially when a few labeled samples are available. The local activities estimation (LAE) method works well with small training sets, but it is more sensitive to the position of the electrodes. Here, we suggest a combination of the LAE and CSP, namely LAE-CSP, which performs better than both methods. MethodsIn LAE-CSP, the EEG signal passes through regularized CSP and LAE spatial filters after band-pass filtering and then the data dimension are reduced based on the physiological data. Afterwards, the features are extracted using fast Fourier transform (FFT). In this work, CSP, FBRCSP, EA-CSP, LAE and LAE-CSP, methods were evaluated and compared. Three sets of motor imagery data from BCI competition III and IV along with Cho et al. dataset, including EEG signals from 28 subjects were used in this study. For each dataset, the training set were selected in 21 different sizes. ResultsLAE-CSP performs better than all tested methods. Particularly it has good performance using only ten labeled samples per class, with an average accuracy of almost 80 %. ConclusionsLAE-CSP reliably enhances the performance of the motor imagery-based brain-computer interfaces. SignificanceThe LAE-CSP takes advantage of the LAE and CSP and compensates for the drawbacks of both methods.

Electroencephalogram-Based Attention Level Classification Using Convolution Attention Memory Neural Network

Attentive learning is an important feature of the learning process. It provides a beneficial learning experience and plays a key role in generating positive learning outcomes. Most studies widely applied electroencephalogram (EEG) to measure human attention level. Although most studies use EEG handcrafted features and statistical methods to classify attention level, a more effective feature learning technique is still needed. In this paper, we aim to analyze participants’ EEG signals through a deep learning model and classify those signals as showing either attentive or inattentive behaviors. To carry out this research, we initially conducted a background study on attention and its detection in EEG. After that, we design a Troxler’s fading experiment and use an EEG device to collect data on participants’ attentive and inattentive behaviors during the test. The collected EEG data will be analyzed using a Convolution Attention Memory Neural Network (CAMNN) model to classify participants’ attention level. The proposed CAMNN model is optimized with Vector-to-Vector (Vec2Vec) modeling, where the model can be learned through deep neural networks in an end-to-end approach. The result shows that our model can achieve 92% accuracy and 0.92 F1 score which outperforms several existing neural network models such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN), Deep Learning with Convolutional Neural Networks (deep ConvNets), and Compact Convolutional Network for EEG-based BCIs (EEGNet). This research can be useful for those who are interested in developing attention level monitoring or biofeedback system in areas such as educational classroom learning, medical research, and industrial operator.

Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016

A brain–computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common noninvasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject nonstationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This article reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications—motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks—are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the article, which may point to future research directions.

Development of a combined, sequential real-time fMRI and fNIRS neurofeedback system to enhance motor learning after stroke

BackgroundAfter stroke, wrist extension dyscoordination precludes functional arm/hand. We developed a more spatially precise brain signal for use in brain computer interface (BCI's) for stroke survivors. New methodCombination BCI protocol of real-time functional magnetic resonance imaging (rt-fMRI) sequentially followed by functional near infrared spectroscopy (rt-fNIRS) neurofeedback, interleaved with motor learning sessions without neural feedback. Custom Matlab and Python code was developed to provide rt-fNIRS-based feedback to the chronic stroke survivor, system user. ResultsThe user achieved a maximum of 71 % brain signal accuracy during rt-fNIRS neural training; progressive focus of brain activation across rt-fMRI neural training; increasing trend of brain signal amplitude during wrist extension across rt-fNIRS training; and clinically significant recovery of arm coordination and active wrist extension. Comparison with existing methodsNeurorehabilitation, peripherally directed, shows limited efficacy, as do EEG-based BCIs, for motor recovery of moderate/severely impaired stroke survivors. EEG-based BCIs are based on electrophysiological signal; whereas, rt-fMRI and rt-fNIRS are based on neurovascular signal. ConclusionThe system functioned well during user testing. Methods are detailed for others' use. The system user successfully engaged rt-fMRI and rt-fNIRS neurofeedback systems, modulated brain signal during rt-fMRI and rt-fNIRS training, according to volume of brain activation and intensity of signal, respectively, and clinically significantly improved limb coordination and active wrist extension. fNIRS use in this case demonstrates a feasible/practical BCI system for further study with regard to use in chronic stroke rehab, and fMRI worked in concept, but cost and some patient-use issues make it less feasible for clinical practice.

P300 Tabanlı Beyin Bilgisayar Arayüzü Sistemlerinde Uyaranlar Arası Sürenin ve Uyaran Yapısının Performansa Etkisi

Elektroensefalografi temelli beyin bilgisayar arayüzü sistemleri kas sistemini kullanamayan hastalar için dış dünya ile iletişimini mümkün kılmaktadır. EEG temelli beyin bilgisayar arayüzü sistemleri için çeşitli beyin sinyal aktiviteleri kullanılmaktadır. Olay odaklı potansiyellerden bir tanesi olan P300 sinyali beyin bilgisayar arayüzü sistemleri için elverişli bir beyin sinyalidir. P300 tabanlı bir beyin bilgsayar arayüzü için en önemli performans parametrelerinden birisi sınıflandırma doğruluk oranıdır. Bu çalışmada satır sütun temelli P300 heceletici yapısındaki değişiklikle daha yüksek doğruluk oranı ile elde edilmesi hedeflenmiştir. P300 heceleticisi matris yapısında ve uyaranların aralık süreleri üzerinde değişiklikler yapılmıştır. Bundan önceki bir çok çalışma P300 heceletici yapısındaki uyaran renk ve biçimlerindeki değişiklikler ile yapılmıştır. Uyaran yapısı ve uyaran aralık sürelerindeki değişikleri kıyaslayıcı P300 heceleticileri ile ilgili çalışmalar yeterli seviyede değildir. Bu çalışmada dört farklı yapıdaki satır sütun bazlı P300 heceletici kullanılarak deneyler yapılmıştır. Deneyler ile toplanan EEG kayıtları ön işlemden geçirildikten sonra adımsal doğrusal ayrışım analizi ile sınıflandırılmıştır. Sınıflandırma neticesinde bu çalışmada karşılaştırılan heceleticilerden; 4x4 satır sütın bazlı P300 heceleticinin 150 ms uyaran aralık süresine sahip olan yapıdaki formu, ortalama doğruluk oranı %84,76 ile en yüksek olarak tespit edilmiştir. En düşük performans ise; 6x6 satır sütın bazlı P300 heceleticinin 300 ms uyaranlar arası geçiş süresine sahip modunda %50,48 olarak gözlenmiştir. Bu çalışma, satır sütun bazlı P300 heceleticisinin uyaran matris yapısındaki değişikliği ve farklı uyaran aralık sürelerinde yapılan deneylerle yüksek doğruluk oranı ile elde edilebileceğini göstermiştir.

A penalized time-frequency band feature selection and classification procedure for improved motor intention decoding in multichannel EEG

Objective. Motor imagery brain–computer interfaces (MI-BCIs) based on electroencephalography (EEG), a promising technology to provide assistance and support rehabilitation of neurological patients with sensorimotor impairments, require a reliable and adaptable subject-specific model to efficiently decode motor intention. The most popular EEG feature extraction algorithm for MI-BCIs is the common spatial patterns (CSP) method, but its performance strongly depends on the predefined frequency band and time segment length for analyzing the EEG signal. Approach. In this work, a novel method for efficiently decoding motor intention for EEG-based BCIs performing multiple frequency band analysis in multiple EEG segments is presented. This decoding algorithm uses raw multichannel EEG data which are decomposed into specific temporal and frequency bands. Features are extracted at each - band by using CSP. Feature selection and classification are simultaneously performed by means of a fast procedure, based on elastic-net regression, which allows for the inclusion of a priori discriminative information into the model. The effectiveness of the proposed method is tested off-line on two public EEG-based MI-BCI datasets and on a self-acquired dataset in two configurations: multiple temporal windows and single temporal window. Main results. The experimental results show that the proposed multiple time-frequency band method yields overall accuracy improvements of up to (average accuracy of 84.8%) as compared to the best current state-of-the-art methods based on filter bank analysis and CSP for MI detection. Also, classification variability is reduced, making the proposed method more robust to intra-subject EEG fluctuations. Significance. This paper presents a novel approach for improving motor intention detection by automatically selecting subject-specific spatio-temporal-spectral features, especially when MI has to be detected against rest condition. This technique contributes to the further advancement and application of EEG-based MI-BCIs for assistance and neurorehabilitation therapy.

A novel onset detection technique for brain–computer interfaces using sound-production related cognitive tasks in simulated-online system

Objective. Self-paced EEG-based BCIs (SP-BCIs) have traditionally been avoided due to two sources of uncertainty: (1) precisely when an intentional command is sent by the brain, i.e., the command onset detection problem, and (2) how different the intentional command is when compared to non-specific (or idle) states. Performance evaluation is also a problem and there are no suitable standard metrics available. In this paper we attempted to tackle these issues. Approach. Self-paced covert sound-production cognitive tasks (i.e., high pitch and siren-like sounds) were used to distinguish between intentional commands (IC) and idle states. The IC states were chosen for their ease of execution and negligible overlap with common cognitive states. Band power and a digital wavelet transform were used for feature extraction, and the Davies–Bouldin index was used for feature selection. Classification was performed using linear discriminant analysis. Main results. Performance was evaluated under offline and simulated-online conditions. For the latter, a performance score called true-false-positive (TFP) rate, ranging from 0 (poor) to 100 (perfect), was created to take into account both classification performance and onset timing errors. Averaging the results from the best performing IC task for all seven participants, an 77.7% true-positive (TP) rate was achieved in offline testing. For simulated-online analysis the best IC average TFP score was 76.67% (87.61% TP rate, 4.05% false-positive rate). Significance. Results were promising when compared to previous IC onset detection studies using motor imagery, in which best TP rates were reported as 72.0% and 79.7%, and which, crucially, did not take timing errors into account. Moreover, based on our literature review, there is no previous covert sound-production onset detection system for spBCIs. Results showed that the proposed onset detection technique and TFP performance metric have good potential for use in SP-BCIs.

Mind over brain, brain over mind: cognitive causes and consequences of controlling brain activity.

Mind over brain, brain over mind: cognitive causes and consequences of controlling brain activity.

Common spatial patterns using analytic signals for EEG-based BCIs

Common spatial patterns using analytic signals for EEG-based BCIs

Localisation of cognitive tasks used in EEG-based BCIs

To provide candidate electrode sites and neurophysiological reference information for cognitive tasks used in brain-computer interfacing research. Six cognitive tasks were tested against the idle state. Data representing the idle state were collected with active cognitive task data during each recording session. Cross subject candidate electrode sites were obtained via a wrapper method based upon a sequential forward floating search algorithm. Source localisation results were obtained using sLORETA software. Spatial feature distributions and localisation results are presented. Primary centres of activity for motor imagery tasks are localised to the pre- and postcentral gyrus. Auditory-based tasks show activity in the middle temporal gyrus. Calculation activity was localised to the left inferior frontal gyrus and right supramarginal gyrus. Navigation imagery produced activity in the precuneus and anterior cingulate cortex. Spatial areas of activation suggest that arithmetic and auditory tasks show promise for pairwise discrimination based on single recording sites. sLORETA significance levels suggest that motor imagery tasks will show greatest discrimination from baseline EEG activity. This is the first study to provide candidate electrode sites for multiple tasks used in brain-computer interfacing.

Evaluation of a wireless wearable tongue–computer interface by individuals with high-level spinal cord injuries

The tongue drive system (TDS) is an unobtrusive, minimally invasive, wearable and wireless tongue–computer interface (TCI), which can infer its users' intentions, represented in their volitional tongue movements, by detecting the position of a small permanent magnetic tracer attached to the users' tongues. Any specific tongue movements can be translated into user-defined commands and used to access and control various devices in the users' environments. The latest external TDS (eTDS) prototype is built on a wireless headphone and interfaced to a laptop PC and a powered wheelchair. Using customized sensor signal processing algorithms and graphical user interface, the eTDS performance was evaluated by 13 naive subjects with high-level spinal cord injuries (C2–C5) at the Shepherd Center in Atlanta, GA. Results of the human trial show that an average information transfer rate of 95 bits/min was achieved for computer access with 82% accuracy. This information transfer rate is about two times higher than the EEG-based BCIs that are tested on human subjects. It was also demonstrated that the subjects had immediate and full control over the powered wheelchair to the extent that they were able to perform complex wheelchair navigation tasks, such as driving through an obstacle course.

Reference Signal Effects on Task Discrimination Algorithms for EEG-based BCIs

Reference Signal Effects on Task Discrimination Algorithms for EEG-based BCIs

α-modulation induced by covert attention shifts as a new input modality for EEG-based BCIs

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