Nonlinear adaptive filtering of stimulus artifact

Noninvasive measurements of somatosensory evoked potentials have both clini cal and research applications. The electrical artifact which results from t he stimulus is an interference which can distort the evoked signal, and int roduce errors in response onset timing estimation, Given that this interfer ence is synchronous with the evoked signal, it cannot be reduced by the con ventional technique of ensemble averaging. The technique of adaptive noise cancelling has potential in this regard however, and has been used effectiv ely in other similar problems. An adaptive noise cancelling filter which us es a neural network as the adaptive element is investigated in this applica tion. The filter is implemented and performance determined in the cancellin g of artifact for in,vivo measurements on the median nerve. A technique of segmented neural network training is proposed in which the network is train ed on that segment of the record Lime window which does not contain the evo ked signal. The neural network is found to generalize well from this traini ng to include the segment of the window containing the evoked signal. Both quantitative and qualitative measures show that significant stimulus artifa ct reduction is achieved.