Graph Convolutional Network (GCN) excels at EEG recognition by capturing brain connections, but previous studies neglect the important EEG feature itself. In this study, we propose MSFR-GCN, a multi-scale feature reconstruction GCN for recognizing emotion and cognition tasks. Specifically, MSFR-GCN includes the MSFR and feature-pool characteristically, with the MSFR consisting of two sub-modules, multi-scale Squeeze-and-Excitation (MSSE) and multi-scale sample re-weighting (MSSR). MSSE assigns weights to channels and frequency bands based on their separate statistical information, while MSSR assigns sample weights based on combined channel and frequency information. The feature-pool, which pools across the feature dimension, is applied after GCN to retain EEG channel information. The MSFR-GCN achieves excellent results in emotion recognition when first tested on two public datasets, SEED and SEED-IV. Than the MSFR-GCN is tested on our self-collected Emotion and Cognition EEG dataset (ECED) for both emotion and cognition classification tasks. The results show MSFR-GCN’s good performance in emotion and cognition classification tasks and reveal the implicit relationship between the two, which may provide aid in the rehabilitation of people with cognitive impairments from an emotional perspective.

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

Fatigued driving is one of the main causes of traffic accidents. The electroencephalogram (EEG)-based mental state analysis method is an effective and objective way of detecting fatigue. However, as EEG shows significant differences across subjects, effectively “transfering” the EEG analysis model of the existing subjects to the EEG signals of other subjects is still a challenge. Domain-Adversarial Neural Network (DANN) has excellent performance in transfer learning, especially in the fields of document analysis and image recognition, but has not been applied directly in EEG-based cross-subject fatigue detection. In this paper, we present a DANN-based model, Generative-DANN (GDANN), which combines Generative Adversarial Networks (GAN) to enhance the ability by addressing the issue of different distribution of EEG across subjects. The comparative results show that in the analysis of cross-subject tasks, GDANN has a higher average accuracy of 91.63% in fatigue detection across subjects than those of traditional classification models, which is expected to have much broader application prospects in practical brain–computer interaction (BCI).

The graph convolutional network shows effective performance in electroencephalogram emotion recognition owing to the ability to capture the brain connectivity. However, the depth information cannot be extracted only through the graph convolutional network structure, and the learning process of the graph convolutional network model ignores the intra-class and the inter-class information. Regarding the above problems, we propose a siamese graph convolutional attention network, named Siam-GCAN, which mainly considers the following two aspects: On the one hand, we use a deep attention layer implemented by multi-head attention mechanism to abstract deeper and valuable features rather than stacking graph convolution layers. On the other hand, we employ the siamese network to cluster the outputs of graph convolutional networks based on Euclidean distance to ensure the learned information has a certain class separability. Experimental results on two public emotional datasets, the SJTU emotion EEG dataset and the SJTU emotion EEG dataset-IV, demonstrate Siam-GCAN outperforms the state-of-the-art baselines in electroencephalogram emotion recognition.