Smoothing of xDAWN spatial filters for robust extraction of event-related potentials

The xDAWN algorithm is well-known as a method for designing spatial filters to improve signal-to-noise ratio and to reduce the dimension of observed EEG signals. This paper proposes a method for spatially smoothing xDAWN spatial filters to give a robustness against small sample problem. The proposed method gives a subspace constraint to the parameter space of the spatial filters. This subspace is given as a basis of the graph Fourier transform on a graph representing geometric structure of the electrodes. The spatial filters found in the subspace are smooth in the spatial domain. The proposed method is evaluated by experiments with artificial signals and BCI datasets.