This paper presents a new method of predicting the P300 component of an EEG signal to recognize the 4 characters in a P300 BCI speller accurately. This method consists of a deep learning model and the nonlinear time-5 frequency features. It is believed that the combination of the deep model network and extracting the nonlinear features 6 of the EEG led this research to a better prediction of the P300 and, therefore, character recognition. Cohen's class 7 distribution is used in order to extract

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... the nonlinear features of the EEG. Evaluating all of the kernels, Butterworth 8 found to be more informative and produced better results. Based on the differences observed between time-frequency 9 responses of target and non-target signals, specific sub-bands are selected to extract seven features. A deep-structured 10 neural network, namely stacked sparse autoencoders, is applied for BCI character recognition. This deep network reduces 11 the dimension of feature space by extracting unsupervised features. Then the features are fed to a Softmax classifier. 12 Afterward, the whole network passes a fine-tuning phase by a supervised backpropagation algorithm. For evaluating the 13 work, Dataset II of BCI Competition III is utilized. Based on the results, this approach would improve the accuracy in 14 both P300 detection and character recognition. This research results in 82.7% and 93.5% accuracy for P300 classification 15 and character recognition, respectively.