Classification of Alcoholics and Non-Alcoholics via EEG Using SVM and Neural Networks

摘要：

The alcoholism is one of psychiatric phenotype, which results from interplay between genetic and environmental factors. Not only it leads to brain defects but also associated cognitive, emotional, and behavioral impairments. It can be detected by analyzing EEG signals. In this research, the power spectrum of the Haar mother wavelet is extracted as features. Then the principle component analysis is applied for dimension reduction of the feature vectors. Finally support vectors machine and neural networks are used for classification. The simulation results show that our proposed method achieves better classification accuracy than the other methods.

关键词： component EEG Biological Signal Processing Pattern Recognition Support Vector Machine Neural Net work

作者: M.R.Nazari Kousarrizi A.Asadi Ghanbari A.Gharaviri M.Teshnehlab M.Aliyari

作者单位: Biomedical Engineering Department K.N.Toosi University of Technology Science and Research branch,Isl Electrical Engineering Department K.N.Toosi University of Technology Tehran,Iran

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）