Melancholia EEG classification based on CSSD and SVM

Jian-Jun Shi; Qing-Wu Yuan; La-Wu Zhou

doi:10.1117/12.913271

1 October 2011 Melancholia EEG classification based on CSSD and SVM

Jian-Jun Shi, Qing-Wu Yuan, La-Wu Zhou

Proceedings Volume 8285, International Conference on Graphic and Image Processing (ICGIP 2011); 82854H (2011) https://doi.org/10.1117/12.913271
Event: 2011 International Conference on Graphic and Image Processing, 2011, Cairo, Egypt

Abstract

It takes an important role to get the disease information from melancholia electroencephalograph (EEG). Firstly, A common spatial subspace decomposition (CSSD) method was used to extract features from 16-channel EEG of melancholia and normal healthy persons. Then based on support vector machines (SVM), a classifier was designed to train and test its classification capability between Melancholia and healthy persons. The results indicated that the proposed method can reach a higher accuracy as 95% in EEG classification, while the accuracy of the method based on wavelet is only 88%.That is, the proposed method is feasible for the melancholia diagnosis and research.

Citation Download Citation

Jian-Jun Shi, Qing-Wu Yuan, and La-Wu Zhou "Melancholia EEG classification based on CSSD and SVM", Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 82854H (1 October 2011); https://doi.org/10.1117/12.913271

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