Research of Saccade-Related EEG: Comparison of Ensemble Averaging Method and Independent Component Analysis

Electroencephalogram (EEG) related to fast eye movement (saccade), has been the subject of application oriented research by our group toward developing a brain-computer interface(BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals on-line. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. However, ensemble averaging is not suitable for BCI

Classification of Single Trial EEG Signals by a Combined Principal + Independent Component Analysis and Probabilistic Neural Network Approach

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