Positive and Negative Emotion Classification Based on Multi-channel.

EEG back propagation neural network decision tree emotion classification k-nearest neighbor support vector machine

Journal

Frontiers in behavioral neuroscience
ISSN: 1662-5153
Titre abrégé: Front Behav Neurosci
Pays: Switzerland
ID NLM: 101477952

Informations de publication

Date de publication:
2021
Historique:
received: 04 06 2021
accepted: 29 07 2021
entrez: 13 9 2021
pubmed: 14 9 2021
medline: 14 9 2021
Statut: epublish

Résumé

The EEG features of different emotions were extracted based on multi-channel and forehead channels in this study. The EEG signals of 26 subjects were collected by the emotional video evoked method. The results show that the energy ratio and differential entropy of the frequency band can be used to classify positive and negative emotions effectively, and the best effect can be achieved by using an SVM classifier. When only the forehead and forehead signals are used, the highest classification accuracy can reach 66%. When the data of all channels are used, the highest accuracy of the model can reach 82%. After channel selection, the best model of this study can be obtained. The accuracy is more than 86%.

Identifiants

pubmed: 34512288
doi: 10.3389/fnbeh.2021.720451
pmc: PMC8428531
doi:

Types de publication

Journal Article

Langues

eng

Pagination

720451

Informations de copyright

Copyright © 2021 Long, Zhao, Wei, Ng, Ni, Chi, Fang, Zeng and Wei.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Fangfang Long (F)

Department of Psychology, Nanjing University, Nanjing, China.

Shanguang Zhao (S)

Centre for Sport and Exercise Sciences, University of Malaya, Kuala Lumpur, Malaysia.

Xin Wei (X)

Institute of Social Psychology, School of Humanities and Social Sciences, Xi'an Jiaotong University, Xi'an, China.
Key & Core Technology Innovation Institute of the Greater Bay Area, Guangdong, China.

Siew-Cheok Ng (SC)

Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.

Xiaoli Ni (X)

Institute of Social Psychology, School of Humanities and Social Sciences, Xi'an Jiaotong University, Xi'an, China.

Aiping Chi (A)

School of Sports, Shaanxi Normal University, Xi'an, China.

Peng Fang (P)

Department of the Psychology of Military Medicine, Air Force Medical University, Xi'an, China.

Weigang Zeng (W)

Key & Core Technology Innovation Institute of the Greater Bay Area, Guangdong, China.

Bokun Wei (B)

Xi'an Middle School of Shaanxi Province, Xi'an, China.

Classifications MeSH