Analysis on Saccade-Related Independent Components by Various ICA Algorithms for Developing BCI

Abstract

Saccade-related electroencephalogram (EEG) signals have 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. In signal processing method for BCI, raw EEG signals are analyzed. In ensemble averaging method which is major EEG analysis is not suitable for processing raw EEG signals. In order to process raw EEG data, we use independent component analysis. This paper presents extraction rate of saccade-related EEG signals by four ICA algorithms and six window size. In terms of extracting rates across ICA algorithms, The JADE and Fast ICA have good results. As you know, calculation time in Fast ICA is faster than calculation time in JADE. Therefore, in this case, Fast ICA is the best in order to extract saccade-related ICs. Next, we focus on extracting rates in each window. The windows not including EEG signals after saccade and the windows which has small window size has better extracting rates.