Periodic component analysis as a spatial filter for SSVEP-based brain-computer interface

Background: Traditional spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost. New method: In this paper, Periodic component analysis (pi CA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The pi CA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates. Results: Data from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results. Comparison with existing methods: The experimental results show that pi CA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions. Conclusions: The results demonstrate that pi CA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence pi CA is a reliable and efficient alternative detection algorithm for SSVEP based brain-computer interface (BCI).