TITLE:
Robust Spatial Filters on Three-Class Motor Imagery EEG Data Using Independent Component Analysis
AUTHORS:
Bangyan Zhou, Xiaopei Wu, Lei Zhang, Zhao Lv, Xiaojing Guo
KEYWORDS:
ICA; Spatial Filter; Motor Imagery; BCI; SVM
JOURNAL NAME:
Journal of Biosciences and Medicines,
Vol.2 No.2,
April
29,
2014
ABSTRACT: Independent Component Analysis (ICA) was often
used to separate movement related independent components (MRICs) from Electroencephalogram
(EEG) data.However, to
obtain robust spatial filters, complex characteristic features, which were manually
selected in most cases, have been commonly used. This study
proposed a new simple algorithm to extract MRICs automatically, which just utilized
the spatial distribution pattern of ICs. The main goal of this study was to show
the relationship between spatial filters performance and designing samples. The
EEG data which containmixed brain states (preparing, motor
imagery and rest) were used to design spatial filters. Meanwhile, the single
class data was also used to calculate spatial filters to assess whether the MRICs
extracted on different class motor imagery spatial filters are similar. Furthermore,
the spatial filters constructed on one subject’s EEG data
were applied to extract the others’ MRICs. Finally, the different spatial filters
were then applied to single-trial EEG to extract MRICs, and Support Vector Machine
(SVM) classifiers were used to discriminate left hand、right-hand and foot imagery movements
of BCI Competition IV Dataset 2a, which recorded four motor imagery data of nine
subjects. The results suggested that any segment of finite motor imagery EEG samples
could be used to design ICA spatial filters, and the extracted MRICs are
consistent if the position of electrodes are the same, which confirmed the robustness and practicality of ICA used in
the motor imagery Brain Computer Interfaces
(MI-BCI) systems.