Cognitive state detection using electroencephalogram (EEG) signals for various tasks has attracted significant research attention. However, it is difficult to further improve the performance of crosssubject cognitive state detection. Further, most of the existing deep learning models will degrade significantly when limited training samples are given, and the feature hierarchical relationships are ignored. To address the above challenges, we propose an efficient interpretation model based on multiple capsule networks for cross-subject EEG cognitive state detection, termed as Efficient EEG-based Multi-Capsule Framework (E3GCAPS). Specifically, we use a selfexpression module to capture the potential connections between samples, which is beneficial to alleviate the sensitivity of outliers that are caused by the individual differences of cross-subject EEG. In addition, considering the strong correlation between cognitive states and brain function connection mode, the dynamic subcapsule-based spatial attention mechanism is introduced to explore the spatial relationship of multi-channel 1D EEG data, in which multichannel 1D data greatly improving the training efficiency while preserving the model performance. The effectiveness of the E3GCAPS is validated on the Fatigue-Awake EEG Dataset (FAAD) and the SJTU Emotion EEG Dataset (SEED). Experimental results show E3GCAPS can achieve remarkable results on the EEG-based cross-subject cognitive state detection under different tasks.

After abusing drugs for long, drug users will experience deteriorated self-control cognitive ability, and poor emotional regulation. This paper designs a closed-loop virtual-reality (VR), motorimagery (MI) rehabilitation training system based on brain-computer interface (BCI) (MI-BCI+VR), aiming to enhance the self-control, cognition, and emotional regulation of drug addicts via personalized rehabilitation schemes. This paper is composed of two parts. In the first part, data of 45 drug addicts (mild: 15; moderate: 15; and severe: 15) is tested with electroencephalogram (EEG) and near-infrared spectroscopy (NIRS) equipment (EEG-NIRS) under the dual-mode, synchronous signal collection paradigm. Using these data sets, a dual-modal signal convolutional neural network (CNN) algorithm is then designed based on decision fusion to detect and classify the addiction degree. In the second part, the MIBCI+ VR rehabilitation system is designed, optimizing the Filter Bank Common Spatial Pattern (FBCSP) algorithm used in MI, and realizing MI-EEG intention recognition. Eight VR rehabilitation scenes are devised, achieving the communication between MI-BCI and VR scene models. Ten subjects are selected to test the rehabilitation system offline and online, and the test accuracy verifies the feasibility of the system. In future, it is suggested to develop personalized rehabilitation programs and treatment cycles based on the addiction degree.

Ocular artifacts in Electroencephalography (EEG) recordings lead to inaccurate results in signal analysis and process. Variational Mode Decomposition (VMD) is an adaptive and completely nonrecursive signal processing method. There are two parameters in VMD that have a great influence on the result of signal decomposition. Thus, this paper studies a signal decomposition by improving VMD based on squirrel search algorithm (SSA). It's improved with abilities of global optimal guidance and opposition based learning. The original seasonal monitoring condition in SSA is modified. The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions. Opposition-based learning is introduced to reposition the position of the population in this stage. It is applied to optimize the important parameters of VMD. GOSSA-VMD model is established to remove ocular artifacts from EEG recording. We have verified the effectiveness of our proposal in a public dataset compared with other methods. The proposed method improves the SNR of the dataset from -2.03 to 2.30.

The non-stationary of the motor imagery electroencephalography(MI-EEG) signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI). The nonstationary of the MI-EEG signal and the changes of the experimental environment make the feature distribution of the testing set and training set deviates, which reduces the classification accuracy of MI-BCI. In this paper, we propose a Kullback-Leibler divergence (KL) -based transfer learning algorithm to solve the problem of feature transfer, the proposed algorithm uses KL to measure the similarity between the training set and the testing set, adds support vector machine (SVM) classification probability to classify and weight the covariance, and discards the poorly performing samples. The results show that the proposed algorithm can significantly improve the classification accuracy of the testing set compared with the traditional algorithms, especially for subjects with medium classification accuracy. Moreover, the algorithm based on transfer learning has the potential to improve the consistency of feature distribution that the traditional algorithms do not have, which is significant for the application of MI-BCI.

The bandwidth of internet connections is still a bottleneck when transmitting large amounts of images, making the image quality assessment essential. Neurophysiological assessment of image quality has highlight advantages for it does not interfere with natural viewing behavior. However, in JPEG compression, the previous study is hard to tell the difference between the electroencephalogram (EEG) evoked by different quality images. In this paper, we propose an EEG analysis approach based on algebraic topology analysis, and the result shows that the difference between Euler characteristics of EEG evoked by different distortion images is striking both in the alpha and beta band. Moreover, we further discuss the relationship between the images and the EEG signals, and the results implied that the algebraic topological properties of images are consistent with that of brain perception, which is possible to give birth to braininspired image compression based on algebraic topological features. In general, an algebraic topologybased approach was proposed in this paper to analyze the perceptual characteristics of image quality, which will be beneficial to provide a reliable score for data compression in the network and improve the network transmission capacity.

Steady-state visual evoked potential (SSVEP) has become a powerful tool for Brain Computer Interface (BCI) because of its high signal-tonoise ratio, high information transmission rate,and minimal user training.At present, the edge information of each region cannot be identified in spatial coding based on SSVEP-BCI technology, and the user experience is poor. To solve this problem, this paper designed a new paradigm to explore the relationship between the fixation point position of continuous sliding and the correlation coefficient ratio in the dualfrequency case. Firstly, the standard sinusoidal signal was employed to simulate the Electroencephalogram (EEG) signal, which verified the reliability of characterizing the amplitude variation of test signal by correlation coefficient.Then, the relationship between the amplitude response of SSVEP and the distance between the fixation point and the stimulus in the horizontal direction was tested by Canonical Correlation Analysis (CCA) and Filter bank CCA (FBCCA). Finally, the experimental data were offline analyzed under the condition of continuous sliding of the fixation point. It is feasible and reasonable to detect the amplitude change of frequency component in SSVEP by utilizing the spatial coding method in this paper to improve the extraction accuracy of spatial information.

A brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) was developed by four-class phase-coded stimuli. SSVEPs elicited by flickers at 60Hz, which is higher than the critical fusion frequency (CFF), were compared with those at 15Hz and 30Hz. SSVEP components in electroencephalogram (EEG) were detected using task related component analysis (TRCA) method. Offline analysis with 17 subjects indicated that the highest information transfer rate (ITR) was 29.80±4.65bpm with 0.5s data length for 60Hz and the classification accuracy was 70.07±4.15%. The online BCI system reached an averaged classification accuracy of 87.75±3.50% at 60Hz with 4s, resulting in an ITR of 16.73±1.63bpm. In particular, the maximum ITR for a subject was 80bpm with 0.5s at 60Hz. Although the BCI performance of 60Hz was lower than that of 15Hz and 30Hz, the results of the behavioral test indicated that, with no perception of flicker, the BCI system with 60Hz was more comfortable to use than 15Hz and 30Hz. Correlation analysis revealed that SSVEP with higher signal-to-noise ratio (SNR) corresponded to better classification performance and the improvement in comfortableness was accompanied by a decrease in performance. This study demonstrates the feasibility and potential of a user-friendly SSVEP-based BCI using imperceptible flickers.