Work

Subject-Specific Channel Selection Using Time Information for Motor Imagery Brain–Computer Interfaces

Year: 2016

Type: article

Abstract: Keeping a minimal number of channels is essential for designing a portable brain–computer interface system for daily usage. Most existing methods choose key channels based on spatial information without optimization of time segment for classification. This paper proposes a novel subject-specific channel selection method based on a criterion called F score to realize the parameterization of both time segment and channel positions. The F score is a novel simplified measure derived from Fisher’s discriminant analysis for evaluating the discriminative power of a group of features. The experimental results on a standard dataset (BCI competition III dataset IVa) show that our method can efficiently reduce the number of channels (from 118 channels to 9 in average) without a decrease in mean classification accuracy. Compared to two state-of-the-art methods in channel selection, our method leads to comparable or even better classification results with less selected channels.

Source: Cognitive Computation

DOI: 10.1007/s12559-015-9379-z

Authors: Yuan Yang, Isabelle Bloch, Sylvain Chevallier, Joe Wiart