Objective. Filter bank canonical correlation analysis (FBCCA) is a widely-used classification approach implemented in steady-state visual evoked potential (SSVEP)–based brain computer interfaces (BCIs). However, conventional detection algorithms for SSVEP recognition problems, including the FBCCA, were usually based on ‘fixed window’ strategy. That’s to say, these algorithms always analyze data with fixed length. This study devoted to enhance the performance of SSVEP-… Show more
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