An Approach to Emotion Recognition Using Brain Rhythm Sequencing and Asymmetric Features

Li, Jia Wen1,2; Chen, Rong Jun1; Barma, Shovan3; Chen, Fei4; Pun, Sio Hang5; Mak, Peng Un6; Wang, Lei Jun1; Zeng, Xian Xian1; Ren, Jin Chang1,7; Zhao, Hui Min1

2022-08-01

10.1007/s12559-022-10053-z

Cognitive Computation   Impact Factor & Quartile

1866-9956

1866-9964

Emotion can be influenced during self-isolation, and to avoid severe mood swings, emotional regulation is meaningful. To achieve this, efficiently recognizing emotion is a vital step, which can be realized by electroencephalography signals. Previously, inspired by the knowledge of sequencing in bioinformatics, a method termed brain rhythm sequencing that analyzes electroencephalography as the sequence consisting of the dominant rhythm has been proposed for seizure detection. In this work, with the help of similarity measure methods, the asymmetric features are extracted from the sequences generated by different channel data. After evaluating all asymmetric features for emotion recognition, the optimal feature that yields remarkable accuracy is identified. Therefore, the classification task can be accomplished through a small amount of channel data. From a music emotion recognition experiment and a public DEAP dataset, the classification accuracies of various test sets are approximately 80-85% when employing an optimal feature extracted from one pair of symmetrical channels. Such performances are impressive when using fewer resources is a concern. Further investigation revealed that emotion recognition shows strongly individual characteristics, so an appropriate solution is to include the subject-dependent properties. Compared to the existing works, this method benefits from the design of a portable emotion-aware device used during self-isolation, as fewer scalp sensors are needed. Hence, it would provide a novel way to realize emotional applications in the future.

Brain rhythm sequencing Electroencephalography Emotion recognition Asymmetric features Symmetrical channels

SCI ; EI

English

Others

National Natural Science Foundation of China[62072122] ; Scientific and Technological Planning Projects of Guangdong Province[2021A0505030074] ; Project for Distinctive Innovation of Ordinary Universities of Guangdong Province[2018KTSCX120] ; Guangdong Colleges and Universities Young Innovative Talents Projects[2018KQNCX138] ; Special Projects in Key Fields of Ordinary Universities of Guangdong Province[2021ZDZX1087] ; Guangzhou Science and Technology Plan Project[202102020857] ; Research Fund of Guangdong Polytechnic Normal University[2022SDKYA015] ; Research Fund of Guangxi Key Lab of Multi-source Information Mining Security[MIMS22-02]

Computer Science ; Neurosciences & Neurology

Computer Science, Artificial Intelligence ; Neurosciences

WOS:000844907500001

SPRINGER

20223612680708

Classification (of information) ; Electroencephalography ; Electrophysiology ; Speech recognition ; Statistical tests

Biomedical Engineering:461.1 ; Medicine and Pharmacology:461.6 ; Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Speech:751.5 ; Information Sources and Analysis:903.1 ; Mathematical Statistics:922.2

Web of Science

1.Guangdong Polytech Normal Univ, Sch Comp Sci, Guangzhou 510665, Peoples R China
2.Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China
3.Indian Inst Informat Technol Guwahati, Dept Elect & Commun Engn, Gauhati 781015, India
4.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518055, Peoples R China
5.Univ Macau, State Key Lab Analog & Mixed Signal VLSI, Taipa 999078, Macao, Peoples R China
6.Univ Macau, Dept Elect & Comp Engn, Taipa 999078, Macao, Peoples R China
7.Robert Gordon Univ, Natl Subsea Ctr, Aberdeen AB21 0BH, Scotland

Li, Jia Wen,Chen, Rong Jun,Barma, Shovan,et al. An Approach to Emotion Recognition Using Brain Rhythm Sequencing and Asymmetric Features[J]. Cognitive Computation,2022.

Li, Jia Wen.,Chen, Rong Jun.,Barma, Shovan.,Chen, Fei.,Pun, Sio Hang.,...&Zhao, Hui Min.(2022).An Approach to Emotion Recognition Using Brain Rhythm Sequencing and Asymmetric Features.Cognitive Computation.

Li, Jia Wen,et al."An Approach to Emotion Recognition Using Brain Rhythm Sequencing and Asymmetric Features".Cognitive Computation (2022).