Presentation 2013-11-22
Classification of EEG Steady-State Visual Evoked Potential Using Stationary Subspace Analysis
Tomoki SHIRATORI, Hironao NAMBA, Takashi MATSUMOTO, Atsushi ISHIYAMA,
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Abstract(in English) We attempted to use signal separation algorithm Stationary Subspace Analysis (SSA) for classification of EEG steady-state visual evoked potential (SSVEP). EEG signals were subjected to Fast Fourier Transform (FFT) to extract features. We used Support Vector Machine (SVM) for classification. We compared accuracy rates of the proposed method with a baseline method where no separation algorithm was used. Of the 12 subjects, 11 showed classification accuracy improvements with the proposed method against the baseline method. The average classification rate improved from 91.4% to 96.2%.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Brain-Computer Interface (BCI) / EEG / Steady-State Visual Evoked potential(SSVEP) / Stationary Subspace Analysis (SSA) / Support Vector Machine(SVM)
Paper # NC2013-48
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Committee NC
Conference Date 2013/11/15(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Classification of EEG Steady-State Visual Evoked Potential Using Stationary Subspace Analysis
Sub Title (in English)
Keyword(1) Brain-Computer Interface (BCI)
Keyword(2) EEG
Keyword(3) Steady-State Visual Evoked potential(SSVEP)
Keyword(4) Stationary Subspace Analysis (SSA)
Keyword(5) Support Vector Machine(SVM)
1st Author's Name Tomoki SHIRATORI
1st Author's Affiliation Faculty of Advanced Science and Engineering, Waseda University()
2nd Author's Name Hironao NAMBA
2nd Author's Affiliation / /
3rd Author's Name Takashi MATSUMOTO
3rd Author's Affiliation
4th Author's Name Atsushi ISHIYAMA
4th Author's Affiliation
Date 2013-11-22
Paper # NC2013-48
Volume (vol) vol.113
Number (no) 315
Page pp.pp.-
#Pages 4
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