Adaptive matched filtering of steady-state visual evoked potentials
C.E. Davila, Richard Srebro, H. Azmoodeh, Ibrahim A. Ghaleb · 2002
The eigenfilter is an FIR filter that maximizes signal-to-noise ratio (SNR). It typically consists of the eigenvector associated with the maximum eigenvalue of the data covariance matrix. Alternately, the eigenfilter may incorporate a linear combination of the dominant covariance matrix eigenvectors. Expressions for the eigenfilter SNR gain are derived. An algorithm for adaptive eigenfiltering is then described which has a computational complexity of O(Md/sup 2/) where M is the eigenfilter length and d is the signal covariance matrix rank. The algorithm is demonstrated via simulations to out-perform a well-known subspace averaging algorithm having similar computational complexity. The eigenfiltering algorithm is then used to obtain estimates of the single trial steady-state visual evoked potential.