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. In order to process raw EEG data in real time, we performed saccade-realted EEG experiments and processed data by using the non-conventional Fast ICA with Reference signal (FICAR). Using the FICAR algorithm, we was able to extract successfully a desired independent components(IC) which are correlated with a reference signal. Visually guided saccade tasks were performed and the EEG signal generated in the saccade was recorded. The EEG processing was performed in three stages: PCA preprocessing and noise reduction, extraction of the desired IC using Wiener filter with reference signal, and post-processing using higher order statistics Fast ICA based on maximization of kurtosis. Form the experimental results and analysis we found that using FICAR it is possible to extract form raw EEG data the saccade-related ICs and to predict saccade in advance by 4[ms] before real movements of eyes occurs. For single trail EEG data we have successfully extracted the desire ICs with recognition rate 72%.