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Discriminative periodic component analysis for SSVEP based BCI
G. R. Kiran Kumar,
Published in Institute of Electrical and Electronics Engineers Inc.
2018
Pages: 427 - 431
Abstract
Spatial filters for steady-state visual evoked potential (SSVEP) detection rely on the purely periodic assumption of the signal components. In this study, we propose discriminative periodic component analysis (\mathbfD\pi \mathbfCA) that takes advantage of the almost periodic nature of SSVEP without depending on ideal rigid templates. \mathbfD\pi \mathbfCA tries to maximize the signal to noise ratio (SNR) of SSVEP components by utilizing the time structure of the stimulus frequencies embedded in the electroencephalogram (EEG) data. The performance of the proposed method was compared with standard canonical correlation analysis (CCA) using data collected from ten subjects. The results suggest that the \mathbfD\pi \mathbfCA provides better detection accuracy compared to standard CCA across various window lengths and subjects. Furthermore, the statistical tests show that the \mathbfD\pi \mathbfCA provides consistent and significant performance improvement than CCA even at short window lengths. © 2018 IEEE.
About the journal
JournalData powered by TypesetSPCOM 2018 - 12th International Conference on Signal Processing and Communications
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open AccessNo
Concepts (11)
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    Biomedical signal processing
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    Electroencephalography
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    ALMOST PERIODIC
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    Canonical correlation analysis
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    DETECTION ACCURACY
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    ELECTROENCEPHALOGRAM (EEG) DATUM
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    PERIODIC COMPONENTS
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    SSVEP
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    STEADY STATE VISUAL EVOKED POTENTIALS
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    STIMULUS FREQUENCY
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    Signal to noise ratio