Proceedings ArticleDOI
Exploiting the temporal structure of EEG data for SSVEP detection
G R Kiran Kumar,M. Ramasubba Reddy +1 more
- pp 1-4
TLDR
In this paper, the authors applied periodic component analysis (nCA) for the extraction of SSVEP components from background electroencephalogram (EEG) data and compared it to standard canonical correlation analysis (CCA).Abstract:
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.read more
Citations
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Journal ArticleDOI
Periodic component analysis as a spatial filter for SSVEP-based brain-computer interface.
TL;DR: periodic component analysis ( π CA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise and provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost.
Journal ArticleDOI
Adaptive canonical correlation analysis for harmonic stimulation frequencies recognition in SSVEP-based BCIs
Sahar Sadeghi,Ali Maleki +1 more
TL;DR: In SSVEP applications with harmonic stimulation frequencies, the adaptive CCA has significantly improved the frequency recognition accuracy in comparison with the popularly standard CCA method.
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