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This paper presents a method that aims to separate auditory event-related potentials (ERP) from noise. In practice, ERPs would be approximated by weighted sums of Principal Component (PCA) basis signals calculated from clean data. Projection of measured signals onto the PCA subspace greatly reduces noise. A second step uses Kalman filtering to optimally combine the PCA filtered signal with the ERP expected before measurement. Much of the power of the proposed algorithm comes from exploiting apriori cross-channel information in the form of a PCA weight covariance matrix. In this paper the performance of the method is quantified using synthetic multi-channel ERP signals to which known amounts of synthetic noise is added to all the channels. The use of synthetic data means and signal and noise are known and so signal-to-noise improvement can be determined. For a wide range of initial SNRs, PCA filtering increases SNR by 10 dB while Kalman filtering yields another 10 dB improvement.

Estimation of Auditory Event-Related Potentials using a Combination of Principal Component Analysis and Kalman Filtering