Improved Discrete Wavelet Analysis and Principal Component Analysis for EEG Signal Processing

Electroencephalogram (EEG) has significant applications on medical diagnosis and Brain Computer Interface (BCI). But the main obstacle of analyzing EEG signal is various types of noises to get actual information. Extracting important features is a key issue in this study. This paper uses the BCI Competition IV 2b motion imagery data, in which we provide a review of various prior art to determine the motion imaginary MI mission. Using machine learning to identify two different movements in the EEG signal, the data from nine subjects were analyzed by principal component analysis (PCA) combined with discrete wavelet (DWT) packet analysis. The extracted DWT feature is input into the support vector machine (SVM) classifier, and the experimental results shows that this method is better than traditional methods with a classification accuracy rate of 86.7%.