Research Progress of EEG-Based Emotion Recognition: A Survey

Abstract

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.