Search

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]

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]

• 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]

• Multidimensional sample entropy combined with multichannel EEG signal calculation can show the difference between pre-ictal and ictal better than ordinary sample entropy. • The calculation speed of multidimensional sample entropy decreases with the increase of time series, and the calculation efficiency can be improved after optimization, which is more suitable for clinical diagnosis. • Bidirectional long short-term memory can be used for both prediction and classification. • Bidirectional long short-term memory neural network provides a epilepsy prediction method that predicts first and then classifies. Epilepsy is a repetitive and transient brain dysfunction caused by abnormal discharge of brain neurons. Sudden epileptic seizures may affect the daily life of patients. Therefore, real-time monitoring and prediction of epilepsy has important clinical meaning. In this paper, the characteristics of M-SampEn were extracted from 23 EEG signals and M-SampEn was specifically optimized to enhance efficiency. Then the Bi-LSTM may predict the trend of M-SampEn. The predicted M-SampEn was classified to determine if an epileptic seizure is imminent. Comparing the classification accuracy, sensitivity, specificity and PPV of SampEn and M-SampEn, M-SampEn is found to have better performance. The prediction time is 5 minutes. The results demonstrate an accuracy of 80.09% and a FPR of 0.26/h for epileptic seizure prediction. The optimized multidimensional sample entropy presented in this paper is more able to distinguish between the normal state and ictal of epilepsy. This paper also proposes a backward prediction method that is different from traditional epileptic seizure prediction. The research provides a high comprehensive performance epileptic prediction method with a F1 score of 0.83. The accuracy of 80.09% and the FPR of 0.26/h prove that the proposed method is able to predict seizures. [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]

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]

• 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]