Research Progress of EEG-Based Emotion Recognition: A Survey

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 me...

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Bibliographic Details
Published inACM computing surveys Vol. 56; no. 11; pp. 1 - 49
Main Authors Wang, Yiming, Zhang, Bin, Di, Lamei
Format Journal Article
LanguageEnglish
Published New York, NY ACM 01.11.2024
Association for Computing Machinery
Subjects
Artificial intelligence
Computing methodologies
Electroencephalography
Emotion recognition
Emotional intelligence
Emotions
Human computer interaction (HCI)
Human-centered computing
Knowledge management
Motion perception
feature analysis
overfitting
multimodal
emotion recognition
EEG
physiological knowledge finding
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Summary: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.
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ISSN:0360-0300
1557-7341
DOI:10.1145/3666002