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Hybrid Template Canonical Correlation Analysis Method for Enhancing SSVEP Recognition under data-limited Condition

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Document pages: 4 pages

Abstract: In this study, an advanced CCA-based algorithn called hybrid templatecanonical correlation analysis (HTCCA) was proposed to improve the performanceof brain-computer interface (BCI) based on steady state visual evoked potential(SSVEP) uuder data-linited condition. The HTCCA method combines the trainingdata from several subjects to construct SSVEP templates. The experinentalresults evaluated on two public benchmark datasets showed that the proposedmethod outperforms the compared methods in both detection accuracy andinformation transfer rate when the number of tuials is small.Considering thatuser-friendly experience will become a key factor for BCI in practicalapplication, it is very necessary to develop effective methods based on limitedEEG samples. This study demonstrates that the proposed method has greatpotential in the application of SSVEP-based brain-computer interfaces.

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