A Stacked Autoencoders Approach for a P300 Speller BCI

This paper addresses a new approach through detecting the P300 and its application to the BCI speller systems. This research employed stacked autoencoders which is based on many autoencoders and a classifier that is regularly a Softmax. This deep structure, decrease the dimension of the data and eventually, the reduced features of the last autoencoder are passed to the Softmax classifier. Subsequently, the parameters of the network would be ameliorated through a fine-tuning phase. Chebyshev Type I, is employed for filtering the EEG signals and using them as an input to the deep neural network. Hyperparameters such as the number of neurons and layers are attained empirically. Therefore, the final structure of the proposed network is 420-210-100-50-20-10-2. To analyze the suggested structure, the second dataset of the third BCI Competition is employed. According to the results, this approach can willingly enhance the character recognition in the BCI speller systems. Thus, the best accuracy percentage according to this research, in an average manner, is 91.5% of both A and B subjects. Consequently, according to the achievements, this method can be comparable to the other state-of-the-art algorithms and, therefore, can improve the recognition rate in the BCI industry.