Suitable ICA Algorithm for Extracting Saccade-Related EEG Signals
Our goal is to develop a novel BCI based on saccade-related EEG signals. It is necessary to analyze raw EEG signals in signal processing methods for BCI. 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, 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.
KeywordsWindow Size Independent Component Analysis Independent Component Analysis Independent Component Analysis Algorithm Independent Component Analysis Method
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