Emotion recognition based on EEG has attracted widespread research interest in the field of brain-computer interfaces. To extract EEG intra- and inter-channel features and find discriminative representations for EEG emotion recognition, we propose EEG-Multilayer Perceptron (EEG-MLP) architecture. EEG-MLP is completely composed of MLPs and mainly consists of two modules, one is a temporal mixer that captures intra-channel (temporal) information, and the other is a channel mixer that captures inter-channel information. The two modules learn knowledge in a parallel manner, and then their outputs are fused to extract global information and classify EEG emotions. We conduct extensive experiments on DEAP dataset. EEG-MLP is first compared with five inter-channel interaction models (related to CNN or GCN) to verify its effectiveness. Then, five other models with similar architecture to EEG-MLP were also contrasted. Experimental results show that EEG-MLP achieves the best performance among the above methods, with accuracies of 94.87% and 95.32% in the valence and arousal dimensions, respectively. In addition, it has a strong discrimination ability for complex categories, and has low requirements for storage resources.

Emotion recognition is a trending topic for research in the area of the brain computer interface (BCI). As an effective signal source, EEG(Electroencephalogram) is widely used in emotion recognition tasks, from which multiple features can be extracted in different domains, such as time domain and frequency domain. However, how to make full use of multiple domain features has become a challenge. In this study, we propose a transformer network for emotion recognition based on Multi-domain EEG features, named MEEG-Transformer. MEEG-Transformer can effectively capture the spatial information with the convolution layer, mine unique information within each domain, and explore the complementary information between features from different domains using self-attention mechanism. Specifically, we extract the features of time domain, frequency domain and wavelet domain respectively, construct the two-dimensional feature matrix of three domains based on the 10-20 system, and merge the three matrices into multi-domain EEG features. Using the DEAP dataset to perform experiments, the proposed model achieves 96.8% and 96.0% recognition accuracy in the arousal and valence dimensions respectively. It is indicated that the proposed method has a strong inspiration for emotion recognition tasks.

Empowering computer systems to automatically recognize human emotions has become an urgent need in the field of human-computer interaction (HCI). Two-dimensional emotion (Valence-Arousal) models are commonly used to represent emotions. Up to now, the correlation between emotion dimensions has rarely been investigated, and subject-independent EEG emotion recognition is still a challenging task. For this purpose, we introduce multi-task learning (MTL) into EEG emotion recognition. MTL learns different emotion dimensions simultaneously and extracts correlation information between dimensions in task-sharing space to coordinate the optimization of multiple emotion dimensions. We further propose Identity based Multi-gate Mixture-of-Experts (IDMMOE), which allocates part of model subspace for each subject in a customized manner according to the subject’s identity. Extensive experiments were conducted on DEAP dataset. Three MTL models were implemented: Shared-Bottom, Multi-gate Mixture-of-Experts, and Customized Gate Control respectively. They were compared with a single-task learning model trained separately on valence and arousal. Experimental results demonstrate that two emotion dimensions are intrinsically related, and MTL acquires such correlation information and improves prediction accuracy in both emotion dimensions. In addition, IDMMOE achieves average accuracies of 89.5% and 89.7% for valence and arousal respectively and it is effective for subject-independent experiment.

Electroencephalography (EEG)-based emotion recognition methods mostly adopt features as many as possible to achieve high performance. However, this is not feasible and flexible for portable or wearable helmet devices because of the limited computing resource and requirement of real-time performance. Aiming at improving EEG emotion recognition performance with features as few as possible, this paper firstly designed a feature called Relative Intensity Ratio Entropy (RIRE), then proposed a novel feature selection method based on greedy algorithm and Max-Relevance and Min-Redundancy(mRMR), termed as Greedy-mRMR. Greedy algorithm is adopted to take top features in the ranking list, and mRMR algorithm is used to select features that are most relevant and minimum redundant. Greedy-mRMR algorithm also uses dynamic termination threshold to guarantee recognition accuracy in each iteration. Experiments were carried on DEAP dataset. SVM classifier achieved average classification accuracy of 91.16% in valence and 91.70% in arousal, by extracting RIRE feature and selecting with Greedy-mRMR algorithm. Experimental results show that RIRE is suitable for EEG emotion recognition and Greedy-mRMR outperforms state-of-the-art methods.

EEG signals directly reflect the active state of the brain, so they are widely used for emotion recognition. At present, many researchers have achieved noteworthy results by using multi-channel EEG signals. However, too many EEG channels will cause slow transmission, high experimental costs, and low efficiency. This paper proposed a grouped dynamic EEG channel selection method based on ReliefF and random forest (RF), termed GDCSBR. We divided all channels into four groups, which provided more choices and flexibility to subsequent dynamic channel selection. GDCSBR selected channels iteratively. With the iteration increased, the number of alternative channels decreased. At each iteration, we adopted the strategy with a minor loss of recognition accuracy. Finally, the results on subject-independent data were taken as the final choice, since there were relatively large differences between subjects. Experiments were carried out on the DEAP dataset. The recognition accuracy for valence reaches 81.27% while 10 channels are selected. As for arousal scale, 11 channels can obtain 82.36% of classification accuracy. In addition, we found that high-frequency bands play a crucial part in emotion recognition, and the selected channels were mostly located in the frontal and parietal lobes. These findings are coincident with previous work. Experimental results demonstrate the effectiveness of the proposed method.