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Electroencephalography (EEG)-based motor imagery (MI) is one of brain computer interface (BCI) paradigms, which aims to build a direct communication pathway between human brain and external devices by decoding the brain activities. In a traditional way, MI BCI replies on a single brain, which suffers from the limitations, such as low accuracy and weak stability. To alleviate these limitations, multi-brain BCI has emerged based on the integration of multiple individuals' intelligence. Nevertheless, the existing decoding methods mainly use linear averaging or feature integration learning from multi-brain EEG data, and do not effectively utilize coupling relationship features, resulting in undesired decoding accuracy. To overcome these challenges, we proposed an EEG-based multi-brain MI decoding method, which utilizes coupling feature extraction and few-shot learning to capture coupling relationship features among multi-brains with only limited EEG data. We performed an experiment to collect EEG data from multiple persons who engaged in the same task simultaneously and compared the methods on the collected data. The comparison results showed that our proposed method improved the performance by 14.23% compared to the single-brain mode in the 10-shot three-class decoding task. It demonstrated the effectiveness of the proposed method and usability of the method in the context of only small amount of EEG data available.

Background: Rapid serial visual presentation (RSVP) based brain-computer interface (BCI) is widely used to categorize the target and non-target images. The available information limits the prediction accuracy of single-trial using single-subject electroencephalography (EEG) signals. New Method. Hyperscanning is a new manner to record two or more subjects' signals simultaneously. So we designed a multi-level information fusion model for target image detection based on dual-subject RSVP, namely HyperscanNet. The two modules of this model fuse the data and features of the two subjects at the data and feature layers. A chunked long and short-term memory artificial neural network (LSTM) was used in the time dimension to extract features at different periods separately, completing fine-grained underlying feature extraction. While the feature layer is fused, some plain operations are used to complete the fusion of the data layer to ensure that important information is not missed., Results: Experimental results show that the F1-score (the harmonic mean of precision and recall) of this method with best group of channels and segment length is 82.76%. Comparison with existing methods. This method improves the F1-score by at least 5% compared to single-subject target detection., Conclusions: Target detection can be accomplished by the two subjects' collaboration to achieve a higher and more stable F1-score than a single subject., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Hybrid EEG-fNIRS brain-computer interface (HBCI) is widely employed to enhance BCI performance. EEG and fNIRS signals are combined to increase the dimensionality of the information. Time windows are used to select EEG and fNIRS singles synchronously. However, it ignores that specific modal signals have their own characteristics, when the task is stimulated, the information between the modalities will mismatch at the moment, which has a significant impact on the classification performance. Here we propose a novel crossing time windows optimization for mental arithmetic (MA) based BCI. The EEG and fNIRS signals were segmented separately by sliding time windows. Then crossing time windows (CTW) were combined with each one segment from EEG and fNIRS selected independently. Furthermore, EEG and fNIRS features were extracted using Filter Bank Common Spatial Pattern (FBCSP) and statistical methods from each sample. Mutual information was calculated for FBCSP and statistical features to characterize the discrimination of crossing time windows, and the optimal window would be selected based on the largest mutual information. Finally, a sparse structured framework of Fisher Lasso feature selection (FLFS) was designed to select the joint features, and conventional Linear Discriminant Analysis (LDA) was employed to perform classification. We used proposed method for a MA dataset. The classification accuracy of the proposed method is 92.52 ± 5.38% and higher than other methods, which shows the rationality and superiority of the proposed method.

Background: Sparse representation-based classification (SRC) has more advantages in motor imagery EEG pattern recognition, and the quality of dictionary construction directly determines the performance of SRC. In this paper, we proposed a two-dimensional dictionary optimization (TDDO) method to directly improve the performance of SRC., New Method: Firstly, an initial dictionary was constructed with multi-band features extracted by filter band common spatial pattern (FBCSP). Then Lasso regression is used to select significant features in each atom synchronously in the horizontal direction, and the KNN-based method is used to clean up noise atoms in the vertical direction. Finally, an SRC method by training samples linearly representing test samples was implemented in classification., Results: The results show the necessity and rationality of TDDO-SRC method. The highest average classification accuracy of 86.5% and 92.4% is obtained on two public datasets., Comparison With Existing Method(s): The proposed method has more superior classification accuracy compared to traditional methods and existing winners' methods., Conclusions: The quality of dictionary construction has a great impact on the robustness of SRC. And compared with the original SRC, the classification accuracy of the optimized TDDO-SRC is greatly improved., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background and Objective: In the brain computer interface (BCI) field, using sub-band common spatial pattern (SBCSP) and filter bank common spatial pattern (FBCSP) can improve the accuracy of classification by selection a specific frequency band. However, in the cross-subject classification, due to the individual differences between different subjects, the performance is limited., Methods: This paper introduces the idea of transfer learning and presents the sub-band target alignment common spatial pattern (SBTACSP) method and applies it to the cross-subject classification of motor imagery (MI) EEG signals. First, the EEG signals are bandpass-filtered into multiple frequency bands (sub-band filtering). Subsequently, the source domain trails are aligned into the target domain space in each frequency band. The CSP algorithm is then employed to extract features among which more representative features are selected by the minimum redundancy maximum relevance (mRMR) approach from each sub-band. Then the features of all sub-bands are fused. Finally, conventional linear discriminant analysis (LDA) algorithm is used for MI classification., Results: Our method is evaluated on Datasets Ⅱa and Ⅱb of the BCI Competition Ⅳ. Compared with six state-of-the-art algorithms, the proposed SBTACSP method performed relatively the best and achieved a mean classification accuracy of 75.15% and 66.85% in cross-subject classification of Datasets Ⅱa and Ⅱb respectively., Conclusion: Therefore, the combination of sub-band filtering and transfer learning achieves superior classification performance compared to either one. The proposed algorithms will greatly promote the practical application of MI based BCIs., Competing Interests: Declaration of Competing Interest The authors declare that there is no conflict of interests regarding the publication of this article., (Copyright © 2021. Published by Elsevier B.V.)

Fatigued driving is one of the main causes of traffic accidents. The electroencephalogram (EEG)-based mental state analysis method is an effective and objective way of detecting fatigue. However, as EEG shows significant differences across subjects, effectively "transfering" the EEG analysis model of the existing subjects to the EEG signals of other subjects is still a challenge. Domain-Adversarial Neural Network (DANN) has excellent performance in transfer learning, especially in the fields of document analysis and image recognition, but has not been applied directly in EEG-based cross-subject fatigue detection. In this paper, we present a DANN-based model, Generative-DANN (GDANN), which combines Generative Adversarial Networks (GAN) to enhance the ability by addressing the issue of different distribution of EEG across subjects. The comparative results show that in the analysis of cross-subject tasks, GDANN has a higher average accuracy of 91.63% in fatigue detection across subjects than those of traditional classification models, which is expected to have much broader application prospects in practical brain-computer interaction (BCI).

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. The comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2019 Hong Zeng et al.)

In this paper we first point out a fatal drawback that the widely used Granger causality (GC) needs to estimate the autoregressive model, which is equivalent to taking a series of backward recursive operations which are infeasible in many irreversible chemical reaction models. Thus, new causality (NC) proposed by Hu et al. (2011) is theoretically shown to be more sensitive to reveal true causality than GC. We then apply GC and NC to motor imagery (MI) which is an important mental process in cognitive neuroscience and psychology and has received growing attention for a long time. We study causality flow during MI using scalp electroencephalograms from nine subjects in Brain-computer interface competition IV held in 2008. We are interested in three regions: Cz (central area of the cerebral cortex), C3 (left area of the cerebral cortex), and C4 (right area of the cerebral cortex) which are considered to be optimal locations for recognizing MI states in the literature. Our results show that: 1) there is strong directional connectivity from Cz to C3/C4 during left- and right-hand MIs based on GC and NC; 2) during left-hand MI, there is directional connectivity from C4 to C3 based on GC and NC; 3) during right-hand MI, there is strong directional connectivity from C3 to C4 which is much clearly revealed by NC than by GC, i.e., NC largely improves the classification rate; and 4) NC is demonstrated to be much more sensitive to reveal causal influence between different brain regions than GC.

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. [ABSTRACT FROM AUTHOR]

Distribution discrepancies between different sessions greatly degenerate the performance of video-evoked electroencephalogram (EEG) emotion recognition. There are discrepancies since the EEG signal is weak and non-stationary and these discrepancies are manifested in different trails in each session and even in some trails which belong to the same emotion. To this end, we propose a Coupled Projection Transfer Metric Learning (CPTML) model to jointly complete domain alignment and graph-based metric learning, which is a unified framework to simultaneously minimize cross-session and cross-trial divergences. By experimenting on the SEED_IV emotional dataset, we show that (1) CPTML exhibits a significantly better performance than several other approaches; (2) the cross-session distribution discrepancies are minimized and emotion metric graph across different trials are optimized in the CPTML-induced subspace, indicating the effectiveness of data alignment and metric exploration; and (3) critical EEG frequency bands and channels for emotion recognition are automatically identified from the learned projection matrices, providing more insights into the occurrence of the effect. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) signal classification is a crucial part in motor imagery brain–computer interface (BCI) system. Traditional supervised learning methods have performed well pleasing in EEG classification. Unfortunately, the unlabeled samples are easier to collect than labeled samples. In addition, recent studies have shown that it may degenerate performance of semi-supervised learning by exploiting unlabeled samples without selection. To address these issues, a novel semi-supervised broad learning system with transfer learning (TSS-BLS) is proposed in this paper. First, the pseudo-labels of unlabeled samples are obtained using the joint distribution adaptation algorithm. TSS-BLS is then constructed by an improved manifold regularization framework containing both labeled and pseudo-label information. Finally, the effectiveness of the proposed TSS-BLS is evaluated on three BCI competition datasets and four benchmark datasets from UCI repository and compared with seven state-of-the-art algorithms, including ELM, SS-ELM, HELM, SVM, LapSVM, BLS and GSS-BLS. Experimental results show that the performance of TSS-BLS is superior to BLS and GSS-BLS on average. It is thereby shown that TSS-BLS is safe and efficient for EEG classification. [ABSTRACT FROM AUTHOR]

In electroencephalography (EEG)-based affective brain–computer interfaces (aBCIs), there is a consensus that EEG features extracted from different frequency bands and channels have different abilities in emotion expression. Besides, EEG is so weak and non-stationary that easily causes distribution discrepancies for EEG data collected at different times; therefore, it is necessary to explore the affective activation patterns in cross-session emotion recognition. To address these two problems, we propose a self-weighted semi-supervised classification (SWSC) model in this article for joint EEG-based cross-session emotion recognition and affective activation patterns mining, whose merits include: 1) using both the labeled and unlabeled samples from different sessions for better capturing data characteristics; 2) introducing a self-weighted variable to learn the importance of EEG features adaptively and quantitatively; and 3) mining the activation patterns including the critical EEG frequency bands and channels automatically based on the learned self-weighted variable. Extensive experiments are conducted on the benchmark SEED_IV emotional dataset and SWSC obtained excellent average accuracies of 77.40%, 79.55%, and 81.52% in three cross-session emotion recognition tasks. Moreover, SWSC identifies that the Gamma frequency band contributes the most and the EEG channels in prefrontal, left/right temporal, and (central) parietal lobes are more important for cross-session emotion recognition. [ABSTRACT FROM AUTHOR]

Emotion recognition based on Electroencephalogram (EEG) has attracted much attention in brain-computer interaction. However, most existing methods usually focus on amplitude and spectrum of the EEG signal, leading to sub-optimal performances due to the insufficiency in modelling the complex intrinsic information of neural integration. To address this issue, this paper proposes to capitalize on the largely neglected phase synchronization (PS) between EEG channels which reflects the intrinsic rhythmic interactions between different channels in neural integration. Specifically, this paper develops a simple and novel feature extraction method which calculates the PS based sparse representation features to analyze emotion states. First, the EEG phase synchronization indexes (PSI) of all channel pairs are estimated as features to distinguish different emotions, since certain topographical maps on PSI reveal specific emotion states. Then principal component analysis is performed to eliminate redundant and noisy features in PSI. Finally, Sparse Representation based Classification (SRC) furtherly emphasize emotion-related features and restrain useless features. For the benchmark affective EEG dataset DEAP, the proposed method based on no-overlapping EEG features achieve an average accuracy of 94.5%, 87.61%, and 67.04% for the classification tasks respectively on two, three and four emotions, demonstrating the superiority over state-of-the-art emotion classification methods. [ABSTRACT FROM AUTHOR]

Since Electroencephalogram (EEG) is resistant to camouflage and contains abundant neurophysiological information, it shows significant superiorities in objective emotion recognition, making EEG-based emotion recognition become a hot research field in brain-computer interface research. However, EEG is generally non-stationary and has a low signal-to-noise ratio, which is difficult to analyze. Inspired by the consensus that exploring a discriminative subspace representation usually helps to capture the semantic information of EEG data, in this paper we propose a Graph Adaptive Semi-supervised Discriminative Subspace Learning (GASDSL) model for EEG-based emotion recognition. GASDSL aims to explore a discriminative subspace in which the intra-class scatter decreases while the inter-class separability increases. The adaptive maximum entropy graph construction and semi-supervised subspace emotional state prediction are adopted to mediate the discriminative subspace learning. Extensive comparative studies on the SEED-IV and SEED-V datasets depict that 1) GASDSL achieved satisfactory emotion recognition accuracy compared with other semi-supervised learning models, 2) the discriminative abilities of both the learned maximum entropy graph and subspace are improved as the model iterates, and 3) the features extracted from the Gamma band, the left/right temporal, prefrontal, and (central) parietal lobes contributed more to emotion recognition based on the spatial-frequency pattern analysis results. [ABSTRACT FROM AUTHOR]

• The predicted pseudo-labels of samples were used to obtain subdomains in the target domain. • Differential Entropy (DE) features extracted from various frequency bands were represented as a set of characteristic matrixes. • Subdomain Associate Loop (SAL) was proposed as a domain adaptation loss criterion. Developing robust cross-subject or cross-session EEG-based affective models is a key issue in affective brain-computer interfaces, which often suffer from the individual differences and non-stationarity of EEG. Aiming at generalizing the affective model across subjects and sessions, this paper proposes a novel transfer learning strategy with Deep Subdomain Associate Adaptation Network (DSAAN) for EEG emotion recognition. Domain was divided into subdomains according to the sample labels, and the source domain use the true sample labels while the target domain use the predicted pseudo-labels. DSAAN was established as a transfer network by aligning the relevant subdomain distributions based on Subdomain Associate Loop (SAL). The adaptation of networks was achieved by minimizing the summation of source domain classification loss and SAL loss. For the purpose of verifying the generalization of DSAAN, we carried out the cross-session and cross-subject EEG emotion recognition experiments on benchmark SEED and DEAP. Compared with existing domain adaptation methods, the DSAAN achieved outstanding classification results. [ABSTRACT FROM AUTHOR]

A brain–computer interface (BCI) based on motor imagery (MI) translates the subject’s motor intention into a control signal through classifying electroencephalogram (EEG) patterns of different imagination tasks, for example, hand movements. Auto-regression (AR) model is one of the popular methods to describe motor imagery patterns, which is widely used by researchers to resolve subject’s motor intention. In this paper, we use joint regression (JR) model and propose an algorithm by combining the coefficients of JR model and spectral powers at two specific frequencies to classify different MI patterns. The algorithm produces a classification accuracy of 90 % on the training data of one subject from BCI2003 Data set III and 80 % on the test data. The results are better than that by using AR model. We also apply the algorithm to MI tasks of one subject in our laboratory, and the classification accuracy can reach 97.86 % on the test data. The results demonstrate that the combination of JR model and spectral powers can achieve much higher accuracy for classification of MI tasks. [ABSTRACT FROM AUTHOR]

• A tensor model of the dynamic brain functional network is proposed. • This method relies on the changing of the interaction of various brain regions. • Orthogonal decomposition of partially symmetric tensors can extract MI features. • The identification electrode is located near Cz during the MI period. The classification of motor imagery (MI) task based on Electroencephalography (EEG) is an important problem in brain-computer interface (BCI) system. The high-precision classification of MI is a challenging task in which the process of feature extraction is crucial step. In this work, we propose a tensor model of a dynamic brain functional network (DBFN) to decode motion intentions. First, we construct the brain functional network in each small window. Then, the BFN of each time window is superimposed into a DBFN tensor with time as the axis. A tensor decomposition method with orthogonal and partial symmetric constraints is used to analyze the DBFN. Finally, the core tensor features are used as an input of the extreme learning machine (ELM) for classification. The results show that the proposed method is better than the degree, clustering coefficient of network, and principal component analysis of DBFN matrix model and the average accuracies are improved by 17.33%, 12.91%, and 17.5% under ELM, respectively. Moreover, the classification accuracy of the proposed method has the lowest variance, i.e., 5.96, indicating that the core tensor features are more adaptable to the subjects. The proposed method has the highest accuracy of 95% under both ELM and support vector machine (SVM). The average accuracy rates of ELM and SVM are 87.08% and 85.83%, respectively. The proposed method effectively extracts the EEG signal characteristics of MI and has strong robustness. This provides a reference for further research on the feature extraction algorithm of BCI. [ABSTRACT FROM AUTHOR]

Emotion recognition has a wide range of potential applications in the real world. Among the emotion recognition data sources, electroencephalography (EEG) signals can record the neural activities across the human brain, providing us a reliable way to recognize the emotional states. Most of existing EEG-based emotion recognition studies directly concatenated features extracted from all EEG frequency bands for emotion classification. This way assumes that all frequency bands share the same importance by default; however, it cannot always obtain the optimal performance. In this paper, we present a novel multi-scale frequency bands ensemble learning (MSFBEL) method to perform emotion recognition from EEG signals. Concretely, we first re-organize all frequency bands into several local scales and one global scale. Then we train a base classifier on each scale. Finally we fuse the results of all scales by designing an adaptive weight learning method which automatically assigns larger weights to more important scales to further improve the performance. The proposed method is validated on two public data sets. For the "SEED IV" data set, MSFBEL achieves average accuracies of 82.75%, 87.87%, and 78.27% on the three sessions under the within-session experimental paradigm. For the "DEAP" data set, it obtains average accuracy of 74.22% for four-category classification under 5-fold cross validation. The experimental results demonstrate that the scale of frequency bands influences the emotion recognition rate, while the global scale that directly concatenating all frequency bands cannot always guarantee to obtain the best emotion recognition performance. Different scales provide complementary information to each other, and the proposed adaptive weight learning method can effectively fuse them to further enhance the performance. [ABSTRACT FROM AUTHOR]