Spatio-spectral feature selection based on robust mutual information estimate for Brain Computer Interfaces.
This paper addresses the issue of selecting optimal spatio-spectral features, which is key to high performance motor imagery (MI) classification that is in turn one of the central topics in EEG-based brain computer interfaces. In particular, this work proposes a novel method which first formulates the selection of features as maximizing mutual information between class labels and features. It then uses a robust estimate of mutual information, within a filter-bank and common spatial pattern feature extraction framework, to select an effective feature set. We have assessed the proposed method on both BCI Competition IV Set I and a separate data set collected in our lab from 7 healthy subjects. The results indicate the method is effective in selecting optimal spatial-spectral features for classification.
Zhang
Haihong
H
Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Connexis, Singapore. hhzhang@i2r.a-star.edu.sg
Ang
Kai Keng
KK
Guan
Cuntai
C
Wang
Chuanchu
C
D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D006801:Humans; D014584:User-Computer Interface