Research on Relationship between Saccade-Related EEG Signals and Selection of Electrode Position by Independent Component Analysis

Our goal is to develop a novel BCI based on an eye movements system employing EEG signals on-line. Most of the analysis on EEG signals has been performed using ensemble averaging approaches. However, in signal processing methods for BCI, raw EEG signals are analyzed.

In order to process raw EEG signals, we used independent component analysis(ICA).

Previous paper presented extraction rate of saccade-related EEG signals by five ICA algorithms and eight window size.

However, three ICA algorithms, the FastICA, the NG-FICA and the JADE algorithms, are based on 4th order statistic and AMUSE algorithm has an improved algorithm named the SOBI. Therefore, we must re-select ICA algorithms.

In this paper, Firstly, we add new algorithms; the SOBI and the MILCA. Using the Fast ICA, the JADE, the AMUSE, the SOBI, and the MILCA. The SOBI is an improved algorithm based on the AMUSE and uses at least two covariance matrices at different time steps. The MILCA use the independency based on mutual information. We extract saccade-related EEG signals and check extracting rates.

Secondly, we check relationship between window sizes of EEG signals to be analyzed and extracting rates.

Thirdly, we researched on relationship between Saccade-related EEG signals and selection of electrode position by ICA. In order to develop the BCI, it is important to use a few electrode. In previous studies, we analyzed EEG signals using by 19 electrodes. In this study, we checked various combination of electrode.