Visual Evoked Potentials' Non Linear Adaptive Filtering Based on Three Layers Perceptron

Single visual evoked potentials (VEP) are very weak and noisy signals. For this reason, powerful extraction tools are needed to improve their clinical use. In this paper, we present two methods for filtering VEP: a linear one, based on the adaptive noise canceller scheme where the input signals represent successive recorded VEP, and a new non-linear method which exploits the neural network property to give a universal approximation of any non-linear function as complex as the VEP signal. We test the effectiveness of these methods and compare their performance to conventional averaging traditionally used in hospitals. As ♣ corresponding author: [email protected] 2760 El-Mehdi Hamzaoui et al compared to adaptive linear filtering or conventional averaging techniques, no assumptions are required on the VEP responses such as the stationarity of the signal and the white Gaussian nature of the background. The results obtained show that non-linear filtering performs efficient VEP extraction based on just a few single responses. This is very significant in terms of saving time especially when recording data from elders and children.