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Proceedings Paper

Melancholia EEG classification based on CSSD and SVM
Author(s): Jian-Jun Shi; Qing-Wu Yuan; La-Wu Zhou
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Paper 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.

Paper Details

Date Published: 1 October 2011
PDF: 7 pages
Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 82854H (1 October 2011); doi: 10.1117/12.913271
Show Author Affiliations
Jian-Jun Shi, Hunan Vocational College of Railway Technology (China)
Qing-Wu Yuan, Hunan Vocational College of Railway Technology (China)
La-Wu Zhou, Hunan Univ. (China)


Published in SPIE Proceedings Vol. 8285:
International Conference on Graphic and Image Processing (ICGIP 2011)
Yi Xie; Yanjun Zheng, Editor(s)

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