MENTAL EEG ANALYSIS USING WAVELET TRANSFORM AND PRINCIPAL COMPONENT ANALYSIS
小波和主分量分析方法研究思维脑电

To explore the relationship between spontaneous EEG and cognitive tasks. An algorithm called WPCA, which is based on wavelet transform and principal component analysis (PCA), was used to process the six channel EEG. The indices of spectral energy and variation rate were calculated, analyzed and stated. The result shows that the proposed WPCA algorithm not only has a good character in noise removing, but also in centralizing the component energy and decreasing the data dimension. The analysis of EEG component revealed the relationship between individual EEG and the mental tasks' kinds, complexity and attention. The result of the NN classification shows the efficiency of the WPCA method. The research is applicable to localize and classify the cognitive tasks and to study the mental function .