EEG Signal Classification Using Power Spectral Features and linear Discriminant Analysis: A Brain Computer Interface Application

Abstract

Biological signal processing offers an alternative to improve life quality in handicapped patients. In this sense is possible, to control devices as wheel chairs or computer systems. The signals that are usually used are EMG, EOG and EEG. When the lost of ability is severe the use of EMG signals is not possible because the person had lost, as in the case of ALS patients, the ability to control his body. EOG offers low resolution because the technique depends of many external and uncontrollable variables of the environment. This work shows the design of a set of algorithms capable to classify brain signals related to imaginary motor activities ( left and right hand imaginary). First, digital signal processing is used to select and extract discriminant features, using parametrical methods for the estimation of the power spectral density and the Fisher criterion for separability. The signal is then classified, using linear discriminant analysis. The results show that is possible to obtain good performance with error rates as low as 13% and that the use of parametrical methods for Spectral Power Density estimation can improve the accuracy of the Brain Computer Interface.