Multireference adaptive noise canceling applied to the EEG

Abstract—The technique of multireference adaptive noise canceling (MRANC) is applied to enhance transient nonstationarities in the elec-troencephalogram (EEG), with the adaptation implemented by means of a multilayer-perceptron artificial neural network (ANN). The method was applied to recorded EEG segments and the performance on documented nonstationarities recorded. The results show that the neural network (nonlinear) gives an improvement in performance (i.e., signal-to-noise ratio (SNR) of the nonstationarities) compared to a linear implementation of MRANC. In both cases an improvement in the SNR was obtained. The advantage of the spatial filtering aspect of MRANC is highlighted when the performance of MRANC is compared to that of the inverse auto-regressive filtering of the EEG, a purely temporal filter. Index Terms—Adaptive filters, electroencephalography, neural network applications, nonlinear filters. I