Single-trial P300 classification using deep belief networks for a BCI system

Sergio A. Cortez, Christian Flores, Javier Andreu-Perez
2020 2020 IEEE XXVII International Conference on Electronics, Electrical Engineering and Computing (INTERCON)  
A brain-computer interface (BCI) aims to provide their users the capability to interact with machines only through their though processes. BCIs targeted at subjects with mild and severe motor impairments are of special interest since this kind of technology would improve their lifestyles. This paper focuses on the classification of the P300 waveform from single trials in EEG to be used in a BCI using deep belief networks. This deep learning algorithm has the capability to identify relevant
more » ... ntify relevant features automatically from the subject's EEG data, making its training requiring less preprocessing stages. The network is tested on healthy subjects and post-stroke victims. The highest accuracy achieved was of 91.6% for a healthy subject and 88.1% for a post-stroke victim. Index Terms-brain-computer interface, stroke victims, EEG, deep belief networks
doi:10.1109/intercon50315.2020.9220255 fatcat:v2xi6mgsfbdknk2fuk4qlevdby