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
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
720451Informations 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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