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The reconstruction from Electroencephalography (EEG) signals to the corresponding image, also named RE2I, plays an important role in promoting the practical applications of brain media. However, due to the low signal-to-noise ratio (SNR) and the significant individual differences of EEG signals, extracting the underlying semantic features of EEG signals and designing a high-performance framework to implement RE2I tasks remain huge challenges. In this study, we propose a DM-RE2I framework based on the diffusion model (DM), which contains an EEG-Visual-Residual-Network (EVRNet) module and a Denoising Diffusion Probabilistic Module (DDPM). In DM-RE2I framework, we first extract the EEG semantic feature (ESF) by using the EVRNet, and then reconstruct images with the same semantics as the corresponding EEG by the DDPM. In addition, we also propose the ESF-guided DDPM (EG-DDPM) training and test algorithms for constructing matching relationship between EEG semantic features and images, as well as generating image with the same semantic as EEG semantic feature, respectively. Experimental results show DM-RE2I has a better capability of EEG semantic feature extraction, and could reconstruct the corresponding image with high resolution and high accuracy. • This paper proposes a novel EEG-based semantic feature extraction module EVRNet to extract temporal and spatial information of EEG signals. By introducing a Multi-Kernel Residual Block (MKRB), not only the gradient vanishing/exploding can be solved, but also the latent richer semantic feature in EEG can be extracted by different convolution kernels. In addition, this paper adopts an average pooling to make the EVRNet better robustness and compatibility. • This paper proposes an EEG semantic guided module named EG-DDPM, as well as the training algorithm and the test algorithm of EG-DDPM, and introduces the ConvNeXt Block into Denoising Diffusion Probabilistic Module (DDPM) Network to reconstruct the corresponding images with high resolution. • This paper designs and implements a reconstruction framework from EEG to the corresponding images, DM-RE2I, and validated its performance on two different EEG datasets and different GPUs. [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 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]

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]