A deep neural network classifier for P300 BCI speller based on Cohen's class time-frequency distribution

Abstract

This paper presents a new method of predicting the P300 component of an electroencephalography (EEG) signal to recognize the characters in a P300 brain-computer interface (BCI) speller accurately. This method consists of a deep learning model and the nonlinear time-frequency features. It is believed that the combination of the deep model network and extracting the nonlinear features of the EEG led this research to a better prediction of the P300 and, therefore, character recognition. Cohen's class distribution is used in order to extract the nonlinear features of the EEG. Evaluating all of the kernels, Butterworth found to be more informative and it produced better results. Based on the differences observed between time-frequency responses of target and nontarget signals, specific subbands are selected to extract seven features. A deep-structured neural network, namely stacked sparse autoencoders, is applied for BCI character recognition. This deep network reduces the dimension of feature space by extracting unsupervised features. Then, the features are fed to a Softmax classifier. Afterward, the whole network passes a fine-tuning phase by a supervised backpropagation algorithm. For evaluating the work, Dataset II of BCI Competition III is utilized. Based on the results, this approach would improve the accuracy in both P300 detection and character recognition. This research results in 82.7% and 93.5% accuracy for P300 classification and character recognition, respectively.