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Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks in which the parameters of hidden units are randomly generated and thus the output weights can be analytically calculated. From the hidden to output layer, ELM essentially learns the output weight matrix based on the least squares regression formula that can be used for both classification/regression and dimensionality reduction. In this paper, we impose the orthogonal constraint on the output weight matrix and then formulate an orthogonal extreme learning machine (OELM) model, which produces orthogonal basis functions and can have more locality preserving power from ELM feature space to output layer than ELM. Since the locality preserving ability is potentially related to the discriminating power, the OELM is expect to have more discriminating power than ELM. Considering the case that the number of hidden units is usually greater than the number of classes, we propose an effective method to optimize the OELM objective by solving an orthogonal procrustes problem. Experiments by pairwisely comparing OELM with ELM on three widely used image data sets show the effectiveness of learning orthogonal mapping especially when given only limited training samples. [ABSTRACT FROM AUTHOR]

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]

Neuroelectric signals recorded by micro-electrodes reflect the spontaneous and rhythmic activities of brain neurons. Numerous deep learning frameworks have been designed for various neuroelectric signal decoding tasks, most of which are based on convolutional neural network (CNN) and recurrent neural network (RNN). However, neither CNNs or RNNs can perceive the global dependencies of neural activities in both time and channel dimensions. To address this issue, this paper presents a temporal-channel cascaded transformer network to decode the neural activities of imagined handwriting movements, which can perform imagined handwriting character recognition from spiking activity recorded by two micro-electrode arrays (MEAs). Specifically, we design a temporal-channel cascaded framework and a dense residual transformer encoder structure, which can promote the hierarchical learning and fusion of the temporal and channel features. In addition, a mutual learning strategy of multiple class tokens is proposed to improve classification performance. We conduct performance evaluation experiments on a single-character handwriting-imagination dataset and a sentence handwriting-imagination dataset, which are collected from the public Handwriting BCI dataset. The comparison results demonstrate the superiority of the proposed framework and strategy. Especially in the imagined single-character recognition task, the recognition accuracy of our model can achieve 95.78%, which provides an improvement of + 2 % over the existing state of the art models. [ABSTRACT FROM AUTHOR]

• Simulated microgravity disturbed neural oscillations in the hippocampus of mice. • rTMS treatment significantly modulated theta oscillatory patterns in Hu mice. • rTMS treatment could ameliorate the disturbance of oscillatory patterns in Hu mice. • A potential underlying mechanism was associated with improving the E/I balance. Microgravity, as a part of the stress of space flight, has several negative effects on cognitive functions. Repetitive transcranial magnetic stimulation (rTMS), as a novel non-invasive technique, could be an effective approach to alleviated cognitive decline, applied in both preclinical and clinical studies. Neural oscillations and their interactions are involved in cognitive functions and support the communication of neural information. The neural oscillation could be a window from which we may understand what happens in the brain. The current study aimed to explore if 15 Hz rTMS plays a neural modulation role in a mouse model of hindlimb unloading. We hypothezed that rTMS can improve the cognitive and neural oscillatory deficits induced by hindlimb unloading via maintaining the balance between glutamatergic and GABAergic systems. Our data show that rTMS can significantly alleviate behavior deficits, modulate theta oscillation, improve the disturbed power distribution of theta oscillation and the decreased strength of Cross-Frequency Coupling in the dentate gyrus region, and effectively mitigated the blocked communication of neural information in the perforant pathway (PP)―dentate gyrus (DG) neural pathway in Hu mice. Furthermore, biochemical analysis using high-performance liquid chromatography and Western blot assay confirmed that rTMS increases the low expression of glutamate (Glu) and N-Methyl d -Aspartate receptor subtype 2B (NR2B) and decreases the high expression of γ-aminobutyric acid (GABA), 67 KDa isoform of glutamate decarboxylase (GAD67), and GABA type A receptor subunit alpha1 (GABAAR α1) in the hippocampus of Hu mice. Taken together, the results suggest that rTMS plays a significant neural modulation role in the hippocampal neural activity disorders induced by Hu, which possibly depends on rTMS maintaining the balance of glutamatergic and gamma-aminobutyric acidergic (GABAergic) systems. [ABSTRACT FROM AUTHOR]

We injected mice with an adenovirus coated with an interference sequence, and the results showed that GABAergic synapses signaling pathway, Glutamatergic synapses signaling pathway and Cholinergic synapses pathway were damaged.At the mesoscopic level, the phase synchronization and phase amplitude coupling of the oscillating signals and synaptic plasticity in the hippocampus of mice were all impaired. At the macro level, the cognitive memory ability and the maintenance of working memory of mice were abnormal. Neural oscillation pattern provides a potential diagnosis approach for mental disorders. [Display omitted] • Knocking down Disc1 gene is a proper animal model of mental disorders. • Disc1 gene knockdown mice show memory deficits and synaptic plasticity impairments. • Disc1 gene knockdown mice exhibit abnormal neural oscillations in the hippocampus. • Neural oscillation analysis offers a potential diagnosis method for mental disorders. The gene of Disrupted-in-schizophrenia 1 (Disc1) is closely related to mental diseases with cognitive deficits, but there are few studies on the changes in neural oscillations and recognition memory. Neural oscillations plays a key role in the nervous system in a dynamic form, which is closely related to advanced cognitive activities such as information processing and memory consolidation. Hence, we aimed to investigate if Disc1 knockdown disrupted the normal pattern of neural activities in the mouse hippocampus network, and determined if quantitative neural oscillation approach could be a potential diagnostic tool for mental disorders. In the study, we reported that Disc1 gene, downregulated by short-hairpin RNA (shRNA), not only induced anxiety-like behavior and sociability impairment but also damaged both synaptic plasticity and recognition memory in mice. Moreover, Disc1 knockdown mice exhibited evidently abnormal power spectral distributions, reduced phase synchronizations, and decreased phase-amplitude coupling strength compared to that of normal animals. In addition, transcriptome analyses showed that there were clearly transcriptional changes in Disc1 knockdown mice. Altogether, our findings suggest that the abnormal pattern of neural activities in the hippocampus network disrupts information processing and finally leads to the impairments of synaptic plasticity and recognition in Disc1 knockdown mice, which are possibly associated with the obstruction of neurotransmitter transmission. Importantly, the data imply that the analysis of neural oscillation pattern provides a potential diagnosis approach for mental disorders. [ABSTRACT FROM AUTHOR]

Subspace learning represents a group of algorithms to project high dimensional data onto a low dimensional subspace in order to retain the desirable properties and simultaneously reduce the dimensionality. In graph-based subspace learning algorithms, the quality of graph affects the performance of projection matrix learning a lot. Especially when data is noisy or even grossly corrupted, we cannot guarantee that the constructed graph can accurately depict the inner structure of data. Additionally, the widely used two-stage paradigm of learning the projection matrix on a given graph isolates the connection between these two stages. In this paper, we propose a general framework to get rid of these disadvantages by the inspiration of low-rank matrix recovery and spectral regression-based subspace learning. Concretely, by jointly optimizing the objectives of low-rank representation and spectral regression, on one hand we can automatically get the recovered data from noise and the graph affinity matrix and on the other hand we can perform the projection matrix learning. The formulated joint low-rank and subspace learning (JLRSL) framework can be efficiently optimized by the augmented Lagrange multiplier method. We evaluate the performance of JLRSL by conducting extensive experiments on representative benchmark data sets and the results show that the low-rank learning can greatly facilitate the process of subspace learning, leading to robust feature extraction. Moreover, comparison with the state-of-the-arts is performed. [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]

Due to its advantages of high temporal and spatial resolution, the technology of simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) acquisition and analysis has attracted much attention, and has been widely used in various research fields of brain science. However, during the fMRI of the brain, ballistocardiogram (BCG) artifacts can seriously contaminate the EEG. As an unpaired problem, BCG artifact removal now remains a considerable challenge. Aiming to provide a solution, this paper proposed a novel modular generative adversarial network (GAN) and corresponding training strategy to improve the network performance by optimizing the parameters of each module. In this manner, we hope to improve the local representation ability of the network model, thereby improving its overall performance and obtaining a reliable generator for BCG artifact removal. Moreover, the proposed method does not rely on additional reference signal or complex hardware equipment. Experimental results show that, compared with multiple methods, the technique presented in this paper can remove the BCG artifact more effectively while retaining essential EEG information. • A novel GAN-based model is designed (BCGGAN) to remove the BCG artifact in simultaneous EEG-fMRI. • A modular training strategy is proposed to optimize the generator network in the BCGGAN model. • The proposed method does not require additional hardware or reference signal, such as carbon fiber sling or ECG signals. • The proposed method can remove the BCG artifact more effectively while retaining useful physiological information. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • The ratio between target and non-target images is 1:9. • Designed a multi-level information fusion model for target image detection based on dual-subject RSVP. • Accomplished higher and more stable F1-score than single subject. [ABSTRACT FROM AUTHOR]

Emotions play a crucial role in human communication and affect all aspects of human life. However, to date, there have been few studies conducted on how movements under different emotions influence human brain activity and cortico-muscular coupling (CMC). In this study, for the first time, electroencephalogram (EEG) and electromyogram physiological electrical signals were used to explore this relationship. We performed frequency domain and nonlinear dynamics analyses on EEG signals and used transfer entropy to explore the CMC associated with the emotion-movement relationship. To study the transmission of information between different brain regions, we also constructed a functional brain network and calculated various network metrics using graph theory. We found that, compared with a neutral emotional state, movements made during happy and sad emotions had increased CMC strength and EEG power and complexity. The functional brain network metrics of these three emotional states were also different. Much of the emotion-movement relationship research has been based on subjective expression and external performance. Our research method, however, focused on the processing of physiological electrical signals, which contain a wealth of information and can objectively reveal the inner mechanisms of the emotion-movement relationship. Different emotional states can have a significant influence on human movement. This study presents a detailed introduction to brain activity and CMC. • Combine EEG and EMG to study the relationship between emotion and movement. • Movement associated with happy emotions can increase the complexity of the left hemisphere. • Movement associated with sad emotions can increase the complexity of the right hemisphere. • Increased grip strength leads to increased the CMC strength of EEG→EMG. • The network topology of movement under different emotions is significantly different. [ABSTRACT FROM AUTHOR]

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. 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. 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. The proposed method has more superior classification accuracy compared to traditional methods and existing winners' methods. 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. • The selected features are distributed in frequency bands relevant to the MI tasks. • The atoms cleaning method satisfies the principle of dictionary construction. • A novel two-dimensional dictionary optimization algorithm is proposed. • Compared with SRC, TDDO-SRC significantly improves the classification accuracy. [ABSTRACT FROM AUTHOR]

• Transfer learning can reduce the sample calibration time. • Target alignment make the distribution of samples in source and target domains more similar. • Combining Sub-band filtering and target alignment obtain richer frequency information. • The classification performance of this method is optimal. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

• A new experimental paradigm for exploring the different effects of anode tDCS on M1 and SMA. • The tDCS's effect on the brain-SMA is more obvious in the motor preparation stage. • The effect of tDCS is more obvious in the execution of the entire motor imagination task, but not in the motor preparation stage. • The effect of tDCS on the motor area of the brain is significant, especially in the M1. Related experiments have shown that transcranial direct current stimulation (tDCS) anodal stimulation of the brain's primary motor cortex (M1) and supplementary motor area (SMA) can improve the motor control and clinical manifestations of stroke patients with aphasia and dyskinesia. In this study, to explore the different effects of tDCS on the M1 and SMA in motor imagery, 35 healthy volunteers participated in a double-blind randomized controlled experiment. Five subjects underwent sham stimulation (control), 15 subjects underwent tDCS anode stimulation of the M1, and the remaining 15 subjects underwent tDCS anode stimulation of the SMA. The electroencephalogram data of the subjects' left- and right-hand motor imagery under different stimulation paradigms were recorded. We used a functional brain network and sample entropy to examine the different complexities and functional connectivities in subjects undergoing sham-tDCS and the two stimulation paradigms. The results show that tDCS anodal stimulation of the SMA produces less obvious differences in the motor preparation phase, while tDCS anodal stimulation of the M1 produces significant differences during the motor imaging task execution phase. The effect of tDCS on the motor area of the brain is significant, especially in the M1. [ABSTRACT FROM AUTHOR]