Suitable ICA Algorithm for Extracting Saccade-Related EEG Signals
Our goal is to develop a novel BCI based on saccaderelated 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.
Arao Funase Motoaki Mouri Andrzej Cichocki Ichi Takumi
Graduate School of Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan Brain Science Institute, Wako, Saitama 351-0198, Japan
国际会议
The Second International Conference on Cognitive Neurodynamics--2009(第二届国际认知神经动力学会议)
杭州
英文
689-694
2009-11-15（万方平台首次上网日期，不代表论文的发表时间）