A Novel Brain Computer Interface Based on Principle Component Analysis

The main issue to build applicable Brain-Computer Interfaces is the capability to classify the Electroencephalograms (EEG). During the last decade, researchers developed lots of interests in this field. The purpose behind this research is to improve a model for EEG signals analysis. The purpose behind this research is to improve a model for brain signals analysis. We have used high pass filter to remove artifacts, discrete wavelet transform algorithms for feature extraction and statistical features like Mean Absolute Value, Root Mean Square, and Simple Square Integral are used, also we have used principle component analysis to reduce the size of feature vector. It has been depicted from results that the proposed integrated techniques outperform a better performance than methods mentioned in literature.