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Emotion Recognition Based on EEG Brain Rhythm Sequencing Technique
Li, Jia Wen1; Barma, Shovan2; Pun, Sio Hang3; Vai, Mang I.4; Mak, Peng Un5
2022
Source PublicationIEEE Transactions on Cognitive and Developmental Systems
ISSN2379-8920
AbstractThis work proposes a technique that analyzes electroencephalography (EEG) using brain rhythms (δ, θ, α, β, and γ) presented in a sequential format and applies it for emotion recognition. Although brain rhythms are regarded as reliable parameters in EEG-based emotion recognition, to achieve high accuracy by considering fewer optimal multi-channel rhythmic features (MCRFs) has not been addressed in detail. Thus, the rhythm sequence for each channel is generated by choosing the strongest brain rhythm having the maximum instantaneous power for every 200 ms time bin. A k-nearest neighbor (k-NN) classifier is employed for evaluating the rhythmic features extracted from different sequences, and the experimental validation was performed on three well-known emotional databases (DEAP, MAHNOB, and SEED). The results showed that approximately 30% of MCRFs for as high as 87%-92%, achieving high classification accuracies with a small number of data. Further investigation revealed that the Frontal and Parietal regions are active during the emotional process, as consistent as earlier studies. Therefore, the proposed technique demonstrates its availability and reliability for emotion recognition. It also provides a novel solution to find optimal channel-specific rhythmic features in EEG signal analysis.
Keywordbrain rhythm sequencing (BRS) Electroencephalography Electroencephalography (EEG) Emotion recognition emotion recognition. Feature extraction multi-channel rhythmic features (MCRFs) reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD) Rhythm Smoothing methods Time-frequency analysis Very large scale integration
DOI10.1109/TCDS.2022.3149953
URLView the original
Language英語English
Scopus ID2-s2.0-85124721334
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Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.State Key Laboratory of Analog and Mixed-Signal VLSI, University of Macau, Macau 999078, China, with the Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China, and also with the School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China. (e-mail: gzcat29@126.com)
2.Department of Electronics and Communication Engineering, Indian Institute of Information Technology Guwahati, Guwahati 781015, India.
3.State Key Laboratory of Analog and Mixed-Signal VLSI, University of Macau, Macau 999078, China.
4.State Key Laboratory of Analog and Mixed-Signal VLSI, University of Macau, Macau 999078, China, and also with the Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China.
5.Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China.
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Li, Jia Wen,Barma, Shovan,Pun, Sio Hang,et al. Emotion Recognition Based on EEG Brain Rhythm Sequencing Technique[J]. IEEE Transactions on Cognitive and Developmental Systems,2022.
APA Li, Jia Wen,Barma, Shovan,Pun, Sio Hang,Vai, Mang I.,&Mak, Peng Un.(2022).Emotion Recognition Based on EEG Brain Rhythm Sequencing Technique.IEEE Transactions on Cognitive and Developmental Systems.
MLA Li, Jia Wen,et al."Emotion Recognition Based on EEG Brain Rhythm Sequencing Technique".IEEE Transactions on Cognitive and Developmental Systems (2022).
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