Efficient extraction of evoked potentials by combination of Wiener filtering and subspace methods

Abstract

A novel approach is proposed in order to reduce the number of sweeps (trials) required for the efficient extraction of the brain evoked potentials (EP). This approach is developed by combining both the Wiener filtering and the subspace methods. First, the signal subspace is estimated by applying the singular-value decomposition (SVD) to an enhanced version of the raw data obtained by Wiener filtering. Next, estimation of the EP data is achieved by orthonormal projection of the raw data onto the estimated signal subspace. Simulation results show that combination of both methods provides much better capability than each of them separately.