Abstract
The emotion affect every aspect of our daily lives. Because of the high temporal resolution and low cost, EEG is widely used in the fields of emotion recognition. This paper studies the effects of different EEG feature combinations and channel selections on emotion recognition. Three effective features including differential entropy (DE), 1st difference (1st) and fractal dimension (FD) were extracted from the EEG signals, and their performances in the three situations of four emotion classifications, two emotions classification on valence and two emotions classification on arousal were calculated and compared by SVM. Two channel selection methods, including the mean relief channel selection algorithm and the common channel selection algorithm, were used to select the best channel. The results showed that when selecting the top 10 channels, the accuracy of the four emotional states classification rate was approximately 95%. This is significant for reducing the number of electrodes and reducing the complexity of brain-computer interface applications in the future.
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Acknowledgment
This work was supported by the National Basic Research Program of China (973 Program) (No. 2014CB744600), the National Natural Science Foundation of China (Grant No. 61632014, No. 61210010, No. 61300231), the National key foundation for developing scientific instruments (No. 61627808), Program of Beijing Municipal Science \& Technology Commission (No. Z171100000117005), Fundamental Research Funds for the Central Universities (lzujbky-2017-kb08, lzujbky-2017-sp25), Talent Innovation Venture Science and Technology Project of Lanzhou (2015-RC-60), Science and technology innovation project.
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Dang, J., Wang, S., Li, C., Wang, Y., Peng, H. (2019). Research on the Classification and Channel Selection of Emotional EEG. In: Tang, Y., Zu, Q., Rodríguez García, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_2
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