DEMİRER, RÜŞTÜ MURATHALİL ÖZCAN GULCURYUKİO KOSUGI2020-03-132020-03-1320000916-8532https://hdl.handle.net/11413/6310This paper investigates the modeling of nonlinearity on the generation of the single trial evoked potential signal (s-EP) by means of using a mixed radial basis function neural network (M-RBFN). The more emphasis is put on the contribution of spontaneous EEG term to s-EP signal. The method is based on a nonlinear M-RBFN neural network model that is trained simultaneously with the different segments of EEG/EP data. Then, the output of the trained model (estimator) is a both fitted and reduced (optimized) nonlinear model and then provide a global representation of the passage dynamics between spontaneous brain activity and poststimulus periods. The performance of the proposed neural network method is evaluated using a realistic simulation and applied to a real EEG/EP measurement.en-USAttribution-NonCommercial-NoDerivs 3.0 United Stateshttp://creativecommons.org/licenses/by-nc-nd/3.0/us/Visual Evoked PotentialsRadial Basis FunctionsNonlinear System IdentificationAuto-regressive Moving AverageNeural NetworksNöral AğlarDoğrusal Olmayan Sistem TanımlamasıOtomatik Regresif Hareketli OrtalamaArticle00008956920001489569200014