Visual evoked potentials estimation by adaptive noise cancellation with neural-network-based fuzzy inference system

Visual Evoked potentials (VEPs) are time-varying signals typically buried in relatively large background noise known as the electroencephalogram (EEG). An adaptive noise cancellation with neural-network-based fuzzy inference system was used and the NNFIS was carefully designed to model the VEP signal. An advantage of the method in this paper is that no reference signal is required. The NNFIS based on Takagi and Sugeno's fuzzy model has the advantage of being linear-in-parameter, which is able to closely fit any function mapping and can track the dynamic behavior of VEP in a real-time fashion. 4 sets of simulated data indicate that the proposed method is appropriate to estimate VEP. A total of 150 trials are processed to demonstrate the superior performance of the proposed method.