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Exploiting the temporal structure of EEG data for SSVEP detection
, Kiran Kumar G.R.
Published in Institute of Electrical and Electronics Engineers Inc.
Volume: 2018-January
Pages: 1 - 4
Traditional multichannel detection algorithms use reference signals that are a generalisation of the steady-state visual evoked potential (SSVEP) components. This leads to the suboptimal performance of the algorithms. For the first time, periodic component analysis (nCA) has been applied for the extraction of SSVEP components from background electroencephalogram (EEG). Data from six test subjects were used to evaluate the proposed method and compare it to standard canonical correlation analysis (CCA). The results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction, and significantly outperforms traditional CCA even in low SNR conditions. The mean detection accuracy of nCA was higher than CCA across subjects, various window lengths and harmonics. The detection scores obtained from nCA provide reliable discrimination between control and idle states compared to CCA. © 2018 IEEE.
About the journal
JournalData powered by Typeset2018 6th International Conference on Brain-Computer Interface, BCI 2018
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open AccessNo