A convolutional deep learning framework for classification of EEG signals

A brain-computer interface (BCI) is a form of assistive technology that facilitates communication between users and machines by interpreting brain signals. The P300 wave, an event-related potential (ERP) in oddball paradigms, is generated approximately 300 milliseconds after the presentation of the target stimulus selected by the user in the brain. Accurate recognition of these waves in a P300 spelling system enables the user to write letters. Classification of P300 waves in an EEG-based spelling system faces several challenges, including accurate detection of P300 waves and handling the high dimensionality of these signals. This study presents a Convolutional Deep Learning Framework (CDLF) for character recognition using EEG signals. The proposed model uses CNN with a one-dimensional kernel to extract features over time. The proposed model was applied to two public datasets: BCI Competition III dataset II and BCI Competition II dataset IIb. The proposed model showed an average character recognition rate of 95% at epoch 15 for BCI Competition III dataset II and 100% at epoch 15 for BCI Competition II dataset IIb, without using any feature and channel selection methods before classification. The proposed model is promising for brain-computer interface classification applications in the spelling domain.