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Testing the feasibility of EEG signals for emotion recognition

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DOI: 10.23977/acss.2021.050109 | Downloads: 23 | Views: 1137

Author(s)

Jiamin Li 1

Affiliation(s)

1 Northeastern University at Qinhuangdao, Qinhuangdao, Hebei, 066000

Corresponding Author

Jiamin Li

ABSTRACT

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.

KEYWORDS

EEG, emotion recognition, accuracy

CITE THIS PAPER

Jiamin Li. Testing the feasibility of EEG signals for emotion recognition. Advances in Computer, Signals and Systems (2021) 5: 60-66. DOI: http://dx.doi.org/10.23977/acss.2021.050109

REFERENCES

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[2] Sanei, Saeid, and Jonathon A. Chambers. EEG signal processing. John Wiley & Sons, 2013.
[3] Lakshmi, M. R., Prasad, T. V., & Prakash, D. V. C. (2014). Survey on EEG signal processing methods. International Journal of Advanced Research in Computer Science and Software Engineering, 4(1).
[4] Alotaiby, T., Abd El-Samie, F. E., Alshebeili, S. A., & Ahmad, I. (2015). A review of channel selection algorithms for EEG signal processing. EURASIP Journal on Advances in Signal Processing, 2015(1), 1-21.
[5] Hu, L., & Zhang, Z. (Eds.). (2019). EEG signal processing and feature extraction (pp. 1-437). Singapore: Springer Singapore.

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