Convolutional Neural Network for a P300 Brain-Computer Interface to Improve Social Attention in Autistic Spectrum Disorder

A Brain-Computer Interface (BCI) relies on machine learning algorithms to decode the brain signals. An accurate detection of P300 response in electroencephalography (EEG) data can be used to design P300-based BCIs to improve social attention in Autistic Spectrum Disorder (ASD). Recently, there was a growing interest in the application of Convolutional Neural Networks (CNNs) to decode P300 in an end-to-end fashion. However, the complexity of these models needs to be carefully taken into account. In this study, a lightweight CNN previously validated for P300 detection (EEGNet) was used to decode which object ASD participants were paying attention to in a virtual environment. Two learning strategies were deepened: within-session and cross-session trainings. Cross-session training resulted in a higher target object accuracy scoring 92.27% on average across sessions and subjects, and in a lower decoding variability across sessions.