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Testing the feasibility of EEG signals for emotion recognition

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DOI: 10.23977/acss.2021.050109 | Downloads: 18 | Views: 309


Jiamin Li 1


1 Northeastern University at Qinhuangdao, Qinhuangdao, Hebei, 066000

Corresponding Author

Jiamin Li


We use the DEAP data set, perform data preprocessing on it, select only the channels corresponding to Fp1, Fp2, F3 and F4, and extract and merge the relevant EEG information to verify the feasibility of EEG signals for emotion recognition. The number of parameters in each group is reduced to between 1 and 25 utilizing principal component analysis. The linear discriminant model and the Naive Bayes model are also used.


EEG, emotion recognition, accuracy


Jiamin Li. Testing the feasibility of EEG signals for emotion recognition. Advances in Computer, Signals and Systems (2021) 5: 60-66. DOI:


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