Presentation 2012-07-02
Sparsification of Time Windows for EEG Signal Classification During Motor Imagery
Hiroshi HIGASHI, Toshihisa TANAKA,
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Abstract(in English) In an EEG based Brain computer interface (BCI), a time-windowed signal having a fixed finite duration is classified into a class associated with a BCI task. The time window should be designed taking into account the duration when a user performs a task and a brain activity occurs. In order to find the appropriate length of the window that works well for BCI, this paper proposes a method for using a sparse time window being sparse in time domain. This method finds the optimal sparsification for the time window by a criterion based on a common spatial pattern method that is an effective method for feature extraction. By experiments, we show that the proposed method improves classification accuracy of BCI.
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Keyword(in English) Brain computer interface / electroencephalogram / time window
Paper # CAS2012-2,VLD2012-12,SIP2012-34,MSS2012-2
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Conference Date 2012/6/25(1days)
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Registration To Mathematical Systems Science and its applications(MSS)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Sparsification of Time Windows for EEG Signal Classification During Motor Imagery
Sub Title (in English)
Keyword(1) Brain computer interface
Keyword(2) electroencephalogram
Keyword(3) time window
1st Author's Name Hiroshi HIGASHI
1st Author's Affiliation Tokyo University of Agriculture and Technology:RIKEN Brain Science Institute()
2nd Author's Name Toshihisa TANAKA
2nd Author's Affiliation Tokyo University of Agriculture and Technology:RIKEN Brain Science Institute
Date 2012-07-02
Paper # CAS2012-2,VLD2012-12,SIP2012-34,MSS2012-2
Volume (vol) vol.112
Number (no) 116
Page pp.pp.-
#Pages 6
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