Efficiency of SSVEF Recognition from the Magnetoencephalogram - A Comparison of Spectral Feature Classification and CCA-based Prediction

Steady-state visual evoked potentials (SSVEP) are a popular method to control brain-computer interfaces (BCI). Here, we present a BCI for selection of virtual reality (VR) objects by decoding the steady-state vi- sual evoked fields (SSVEF), the magnetic analogue to the SSVEP in the magnetoencephalogram (MEG). In a conventional approach, we performed online prediction by Fourier transform (FT) in combination with a mul- tivariate classifier. As a comparative study, we report our approach to increase the BCI-system performance in an offline evaluation. Therefore, we transfered the canonical correlation analysis (CCA), originally employed to recognize relatively low dimensional SSVEPs in the electroencephalogram (EEG), to SSVEF recognition in higher dimensional MEG recordings. We directly compare the performance of both approaches and con- clude that CCA can greatly improve system performance in our MEG-based BCI-system. Moreover, we find that application of CCA to large multi-sensor MEG could provide an effective feature extraction method that automatically determines the sensors that are informative for the recognition of SSVEFs.