CLASSIFICATION OF SINGLE TRIAL EEG SIGNALS BY A COMBINED PRINCIPAL + INDEPENDENT COMPONENT ANALYSIS AND PROBABILISTIC NEURAL NETWORK APPROACH

Abstract

In this paper, an attempt is made to classify the EEG signals of letter imagery tasks using a combined independent component analysis and probabilistic neural network. The role of the principal/independent component analysis is to mitigate the effect of EOG artifacts within each single-trial EEG pattern. Experimental results show an overall performance improvement of around in terms of the pattern classification accuracy, in comparison with the LPC spectral analysis which is commonly employed in speech recognition tasks. 1.