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

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 cha...

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Bibliographic Details
Published inCognitive computation Vol. 8; no. 3; pp. 505 - 518
Main Authors Yang, Yuan, Bloch, Isabelle, Chevallier, Sylvain, Wiart, Joe
Format Journal Article
LanguageEnglish
Published New York Springer US 01.06.2016
Springer Nature B.V
Springer
Subjects
Algorithms
Artificial Intelligence
Biomedical and Life Sciences
Biomedicine
Brain research
Channels
Classification
Cognitive science
Computation by Abstract Devices
Computational Biology/Bioinformatics
Computer Science
Datasets
Discriminant analysis
Electrodes
Electroencephalography
Human-computer interface
Machine Learning
Methods
Neuroscience
Neurosciences
Parameterization
Segments
Signal and Image Processing
Signal processing
Spatial data
Fisher’s discriminant analysis
Brain–computer interfaces
Channel reduction
EEG
Time information
channel reduction
Fisher's discriminant analysis
time information
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Summary: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.
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ISSN:1866-9956
1866-9964
DOI:10.1007/s12559-015-9379-z