Model-based noise reduction for single trial evoked potentials

Two model-based techniques, Gaussian mixture models with integrated noise component and principal component analysis, are applied to noise reduction for single trial evoked potentials which are buried in noise up to five times stronger than the signal. An empirical study using artificial data is presented and results are compared to the standard technique of averaging.