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Emotion recognition based on electroencephalography (EEG) signals has emerged as a prominent research field, facilitating objective evaluation of diseases like depression and motion detection for heathy people. Starting from the basic concepts of temporal-frequency-spatial features in EEG and the methods for cross-domain feature fusion, this survey then extends the overfitting challenge of EEG single-modal to the problem of heterogeneous modality modeling in multimodal conditions. It explores issues such as feature selection, sample scarcity, cross-subject emotional transfer, physiological knowledge discovery, multimodal fusion methods, and modality missing. These findings provide clues for researchers to further investigate emotion recognition based on EEG signals.
Wang et al. (Tue,) studied this question.