A SSVEP BCI based on Canonical Correlation Analysis

Canonical correlation analysis (CCA) is one of the most popular methods in the field of Brain Computer Interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs). The efficacy of the method has been widely proved, and several variations have been proposed. However, most of the approaches still consider only the first canonical correlation as a feature for classification, which can leave some important information behind. Notably, if the signal shows phase transitions, its informative content can be diffused over more than one coefficient. We show here that considering the first two canonical correlations, instead of the largest one only, can significantly improve classification accuracy without increasing computational load, and that an adjunctive pre-processing step with sinc-windowing can further enhance the results.

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