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Objective: Stroke is the leading cause of disability worldwide. Traditionally, doctors assess stroke rehabilitation assessment, which can be subjective. Therefore, an objective assessment method is required. Methods: In this context, we investigated the changes in brain functional connectivity patterns and corticomuscular coupling in stroke patients during rehabilitation. In this study, electroencephalogram (EEG) and electromyogram (EMG) of stroke patients were collected synchronously at baseline(BL), two weeks after BL, and four weeks after BL. A brain functional network was established, and the corticomuscular coupling relationship was calculated using phase transfer entropy (PTE). Results: We found that during the rehabilitation of stroke patients, the overall connection of the brain functional network was strengthened, and the network characteristic value increased. The average corticomuscular PTE appeared to first decrease and subsequently increase, and the PTE increase in the frontal lobe was significant. Value: In this study, PTE was used for the first time to analyze the relationship between EEG signals in patients with hemiplegia. We believe that our findings contribute to evaluating the rehabilitation of stroke patients with hemiplegia. [ABSTRACT FROM AUTHOR]

Personal identification is particularly important in information security. There are numerous advantages of using electroencephalogram (EEG) signals for personal identification, such as uniqueness and anti-deceptiveness. Currently, many researchers focus on single-dataset personal identification, instead of the cross-dataset. In this paper, we propose a method for cross-dataset personal identification based on a brain network of EEG signals. First, brain functional networks are constructed from the phase synchronization values between EEG channels. Then, some attributes of the brain networks including the degree of a node, the clustering coefficient and global efficiency are computed to form a new feature vector. Lastly, we utilize linear discriminant analysis (LDA) to classify the extracted features for personal identification. The performance of the method is quantitatively evaluated on four datasets involving different cognitive tasks: (i) a four-class motor imagery task dataset in BCI Competition IV (2008), (ii) a two-class motor imagery dataset in the BNCI Horizon 2020 project, (iii) a neuromarketing dataset recorded by our laboratory, (iv) a fatigue driving dataset recorded by our laboratory. Empirical results of this paper show that the average identification accuracy of each data set was higher than 0.95 and the best one achieved was 0.99, indicating a promising application in personal identification.

Various relations existing in Electroencephalogram (EEG) data are significant for EEG feature representation. Thus, studies on the graph-based method focus on extracting relevancy between EEG channels. The shortcoming of existing graph studies is that they only consider a single relationship of EEG electrodes, which results an incomprehensive representation of EEG data and relatively low accuracy of emotion recognition. In this paper, we propose a fusion graph convolutional network (FGCN) to extract various relations existing in EEG data and fuse these extracted relations to represent EEG data more comprehensively for emotion recognition. First, the FGCN mines brain connection features on topology, causality, and function. Then, we propose a local fusion strategy to fuse these three graphs to fully utilize the valuable channels with strong topological, causal, and functional relations. Finally, the graph convolutional neural network is adopted to represent EEG data for emotion recognition better. Experiments on SEED and SEED-IV demonstrate that fusing different relation graphs are effective for improving the ability in emotion recognition. Furthermore, the emotion recognition accuracy of 3-class and 4-class is higher than that of other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recently, electroencephalogram (EEG)-based emotion recognition has attracted increasing interests in research community. The weak, non-stationary, multi-rhythm and multi-channel properties of EEG data easily cause the extracted EEG samples and features contribute differently in recognizing emotional states. However, existing studies either failed to consider both the issues of sample and feature importance or only considered one of them. In this brief, we propose a new model termed sJSFE (semi-supervised Joint Sample and Feature importance Evaluation) to quantitatively measure the sample and feature importance by self-paced learning and feature self-weighting respectively. Experimental results on the SEED-IV data set show that the emotion recognition performance is greatly improved by mining both the sample and feature importance. Specifically, the average accuracy obtained by sJSFE across the three cross-session recognition tasks is 82.45%, which is respectively 3.72% and 7.21% and 10.47% and 18.82% higher than the results of traditional models. Besides, the feature importance vector depicts that the Gamma frequency band contributes the most, and the brain regions of prefrontal, left/right temporal and (central) parietal lobes correlate more to emotion recognition. The sample importance descriptor shows that continual transitions of video types in consecutive trials might weaken the feature-label consistency of the collected EEG data. [ABSTRACT FROM AUTHOR]

Human errors are widely considered among the major causes of road accidents. Furthermore, it is estimated that more than 90% of vehicle crashes causing fatal and permanent injuries are directly related to mental tiredness, fatigue, and drowsiness of the drivers. In particular, driving drowsiness is recognized as a crucial aspect in the context of road safety, since drowsy drivers can suddenly lose control of the car. Moreover, the driving drowsiness episodes mostly appear suddenly without any prior behavioral evidence. The present study aimed at characterizing the onset of drowsiness in car drivers by means of a multimodal neurophysiological approach to develop a synthetic electroencephalographic (EEG)-based index, able to detect drowsy events. The study involved 19 participants in a simulated scenario structured in a sequence of driving tasks under different situations and traffic conditions. The experimental conditions were designed to induce prominent mental drowsiness in the final part. The EEG-based index, so-called "MDrow index" , was developed and validated to detect the driving drowsiness of the participants. The MDrow index was derived from the Global Field Power calculated in the Alpha EEG frequency band over the parietal brain sites. The results demonstrated the reliability of the proposed MDrow index in detecting the driving drowsiness experienced by the participants, resulting also more sensitive and timely sensible with respect to more conventional autonomic parameters, such as the EyeBlinks Rate and the Heart Rate Variability, and to subjective measurements (self-reports). [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]

Brainprint is a new type of biometric in the form of EEG, directly linking to intrinsic identity. Currently, most methods for brainprint recognition are based on traditional machine learning and only focus on a single brain cognition task. Due to the ability to extract high-level features and latent dependencies, deep learning can effectively overcome the limitation of specific tasks, but numerous samples are required for model training. Therefore, brainprint recognition in realistic scenes with multiple individuals and small amounts of samples in each class is challenging for deep learning. This article proposes a Convolutional Tensor-Train Neural Network (CTNN) for the multi-task brainprint recognition with small number of training samples. Firstly, local temporal and spatial features of the brainprint are extracted by the convolutional neural network (CNN) with depthwise separable convolution mechanism. Afterwards, we implement the TensorNet (TN) via low-rank representation to capture the multilinear intercorrelations, which integrates the local information into a global one with very limited parameters. The experimental results indicate that CTNN has high recognition accuracy over 99% on all four datasets, and it exploits brainprint under multi-task efficiently and scales well on training samples. Additionally, our method can also provide an interpretable biomarker, which shows specific seven channels are dominated for the recognition tasks. [ABSTRACT FROM AUTHOR]

Evaluating the effect of stroke rehabilitation based on electroencephalogram (EEG) is still a challenging problem. This paper presents a novel nonlinear dynamic complexity method for the evaluation of stroke rehabilitation effect from EEG signal. Our method calculates the nonlinearly separable degree (NLSD) of EEG signal, and then employs an indicator, called mean nonlinearly separable complexity degree (Mean_NLSD), to efficiently and accurately evaluate therapy effect of stroke patients. This paper under twelve stimuli conditions on eleven patients and eleven control subjects indicates that in general Mean_NLSD is smaller at the lesion regions and that the Mean_NLSD of the control subjects is stochastic. Compared with conventional spectral methods, such as mean power spectral density (PSD), Mean_NLSD is more sensitive and robust. Overall Mean_NLSD may offer a promising approach to facilitate the evaluation of stroke rehabilitation effect. [ABSTRACT FROM PUBLISHER]

Driver fatigue has been under more attention as it is a main cause of traffic accidents. This paper proposed a method which utilized the inter/intra-region phase synchronization and functional units (FUs) to explore whether EEG synchronization changes from the alert state to the fatigue state. Mean phase coherence (MPC) is adopted as a measure for the phase synchronization. In order to find spatial-frequency features associated with mental state, we studied the intra/inter-region phase synchronization of EEG in different frequencies. The major finding is that EEG synchronizations in delta and alpha bands in frontal and parietal lobe are significantly increased as the mental state of the driver shifted from alertness to fatigue. This finding is simultaneously validated by NASA-Task Load Index (TLX) and Karolinska sleepiness scale (KSS). The statistical analysis results suggest MPC may be used to distinguish between alert and fatigue state of mind. In addition, the another contribution of the work indicates a simple and significant spatial-frequency pair of electrodes, i.e., Fz-Oz in delta band, to evaluate driver fatigue. It helps to implement real-world applications with wearable EEG equipment. [ABSTRACT FROM AUTHOR]

• EEG-EMG coherence decreases during music stimuli. • Music can strengthen the causal connection between the brain and muscles. • Musical stimulation enhances the connectivity of the brain. • The influence of melody on the TE in the downward direction(EEG → EMG) is greater than that in the upward direction(EMG → EEG). Music is widely used as an auxiliary treatment for the recovery of motor function and emotional regulation in patients with epilepsy, Parkinson's disease, and stroke. It also has certain positive impacts on physiology and psychology during physical exercise. This study investigates the functional corticomuscular coupling (FCMC) relationship and the changes in the brain functional connection mode in normal people in response to music stimuli when the right hand continuously outputs grip power. Electroencephalography (EEG) and electromyography (EMG) tests are synchronously performed on normal participants with music stimuli, with audiobook stimuli, and without stimulation. The similarity, causality, and direction of the signals are calculated by EEG-EMG coherence and transfer entropy (TE), and the brain functional connectivity network is established to analyze the changes in the coupling relationship between regions of the brain and between different regions of the brain and muscles. It is found that, for the CP2, FC2, and four muscle channels in this study, music stimuli reduce EEG-EMG coherence. In addition, the characteristics of corticomuscular TE and the brain functional connectivity network with music stimuli are quite different from other groups. This paper explores the effects of music stimuli on FCMC from the perspective of physiological electrical signal analysis, which may have a positive impact on future studies of music therapy in neurorehabilitation. [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 bidirectional distillation strategy is proposed to realize the information interaction between hybrid networks. • BIHN optimizes the performance of both CogN and ComN in EEG cognitive recognition by using a bidirectional feedback mode. • Experimental results show the superior performance of BIHN in cognitive recognition compared to many SOTA models. • The BIHN architecture is suitable for various hybrid network pairs related to cognitive and computational networks. Background and objective: Extracting cognitive representation and computational representation information simultaneously from electroencephalography (EEG) data and constructing corresponding information interaction models can effectively improve the recognition capability of brain cognitive status. However, due to the huge gap in the interaction between the two types of information, existing studies have yet to consider the advantages of the interaction of both. Methods: This paper introduces a novel architecture named the bidirectional interaction-based hybrid network (BIHN) for EEG cognitive recognition. BIHN consists of two networks: a cognitive-based network named CogN (e.g., graph convolution network, GCN; capsule network, CapsNet) and a computing-based network named ComN (e.g., EEGNet). CogN is responsible for extracting cognitive representation features from EEG data, while ComN is responsible for extracting computational representation features. Additionally, a bidirectional distillation-based coadaptation (BDC) algorithm is proposed to facilitate information interaction between CogN and ComN to realize the coadaptation of the two networks through bidirectional closed-loop feedback. Results: Cross-subject cognitive recognition experiments were performed on the Fatigue-Awake EEG dataset (FAAD, 2-class classification) and SEED dataset (3-class classification), and hybrid network pairs of GCN + EEGNet and CapsNet + EEGNet were verified. The proposed method achieved average accuracies of 78.76% (GCN + EEGNet) and 77.58% (CapsNet + EEGNet) on FAAD and 55.38% (GCN + EEGNet) and 55.10% (CapsNet + EEGNet) on SEED, outperforming the hybrid networks without the bidirectional interaction strategy. Conclusions: Experimental results show that BIHN can achieve superior performance on two EEG datasets and enhance the ability of both CogN and ComN in EEG processing as well as cognitive recognition. We also validated its effectiveness with different hybrid network pairs. The proposed method could greatly promote the development of brain-computer collaborative intelligence. [ABSTRACT FROM AUTHOR]

A common but easily overlooked affective overlap problem has not been received enough attention in electroencephalogram (EEG)-based emotion recognition research. In real life, affective overlap refers to the current emotional state of human being is sometimes influenced easily by his/her historical mood. In stimulus-evoked EEG collection experiment, due to the short rest interval in consecutive trials, the inner mechanisms of neural responses make subjects cannot switch their emotion state easily and quickly, which might lead to the affective overlap. For example, we might be still in sad state to some extent even if we are watching a comedy because we just saw a tragedy before. In pattern recognition, affective overlap usually means that there exists the feature-label inconsistency in EEG data. To alleviate the impact of inconsistent EEG data, we introduce a variable to adaptively explore the sample inconsistency in emotion recognition model development. Then, we propose a semi-supervised emotion recognition model for joint sample inconsistency and feature importance exploration (SIFIAE). Accordingly, an efficient optimization method to SIFIAE model is proposed. Extensive experiments on the SEED-V dataset demonstrate the effectiveness of SIFIAE. Specifically, SIFIAE achieves 69.10%, 67.01%, 71.50%, 73.26%, 72.07% and 71.35% average accuracies in six cross-session emotion recognition tasks. The results illustrated that the sample weights have a rising trend in the beginning of most trials, which coincides with the affective overlap hypothesis. The feature importance factor indicated the critical bands and channels are more obvious compared with some models without considering EEG feature-label inconsistency. • Affective overlap problem was considered in emotion recognition. • Feature-label inconsistency and feature importance were utilized jointly. • SIFIAE obtained a significant improvement in cross-session emotion recognition. • Obtained sample inconsistency demonstrated affective overlap hypothesis. • Affective activation pattern could be adaptively obtained in our model. [ABSTRACT FROM AUTHOR]