Abstrakt

Reasoning of EEG waveform using Revised Principal Component Analysis (RPCA)

Deepa R, Shanmugam A, Sivasenapathi B

The analysis of brain activity and classification is a prime issue in Electroencephalogram (EEG) signal processing these days. The related exertion has been taken to estimate the brain activity on the basis of non-invasive power spectrum analysis. For this, modified approach involving Revised Principal Component Analysis (RPCA), multipliers and Support Vector Machine (SVM) Classifiers with two distinct features are contrasted to investigate the behavior of brain’s electrical activity of a visual attention. The proposed method of EEG classification can be very useful in predicting the action of brain, analysing the activity of the signal in open or in close condition and it provides better behavior of the frequency. The EEG data has been acquired from a WindDAQ Acquisition and the EEG analysis has been carried out in MATLAB platform to perform the work in this paper.