Neural decoding of code modulated visual evoked potentials by spatio-temporal inverse filtering for brain computer interfaces.

This study addresses neural decoding of a code modulated visual evoked potentials (c-VEPs). c-VEP was recently developed, and applied to brain computer interfaces (BCIs). c-VEP BCI exhibits faster communication speed than existing VEP-based BCIs. In c-VEP BCI, the canonical correlation analysis (CCA) that maximizes the correlation between an averaged signal and single trial signals is often used for the spatial filter. However, CCA does not utilize information of given PN sequence, and hence, the filtered signal may not have properties of PN sequence. In this paper, we propose a decoding method to restore the given PN sequence from the observed VEP. We compare linear and nonlinear spatio-temporal inverse filtering methods. For the linear method, the least mean square error and lasso are used to obtain the filter coefficients. For the non-linear method, the artificial neural network is used. The proposed methods exhibited better decoding performance, and higher classification accuracies than conventional CCA spatial filtered c-VEP BCI.