An optimized deep learning approach based on autoencoder network for P300 detection in brain computer interface systems

Background. Brain computer interface (BCI) systems by extracting knowledge from brain signals provide a connection channel to the outside world for disabled people, without physiological interfaces. Event-related potentials (ERPs) are a specific type of electroencephalography signals and P300 is one of the most important ERP components. The critical part of P300-based BCI systems is classification step. In this research, an approach is proposed for P300 classification based on novel machine learning methods using convolutional neural networks (CNN) and autoencoder networks. Methods. In the pre-processing step, channel selection, data augmentation (by ADASYN method), filtering and base-line drift were done. Then, in the classification step, four different CNN classifiers including CNN1D, CNN2D, CNN1D_Autoencoder, and CNN2D-Autoencoder were used for P300 classification. Results. After implementation and tuning the networks, 92% as a best accuracy was achieved by CNN2D_Autoencoder. This result was achieved with a considerable tradeoff between complexity and stability. Conclusion. The acquired results emphasize the ability of the deep learning methods in P300 classification and approve the advantage of using them in BCI systems. Furthermore, autoencoder versions of CNN networks are more stable and have a faster convergence. Meanwhile, ADASYN is a suitable method for augmentation of P300 data and even ERPs by sustaining the premier feature space without copying data. Practical Implications. Our results can increase the accuracy of P300 detection and simultaneously reduce the volume of data using the proposed model. Consequently, they can improve character recognition in P300-speller systems generally used by amyotrophic lateral sclerosis (ALS) patients.