1D Convolutional Neural Network approach to classify voluntary eye blinks in EEG signals for BCI applications

The goal of this paper is to develop a Brain Computer Interface (BCI) based on voluntary eye blinks decoding. In particular, the study was focused on the signals generated in the cortex by eye blinking, which can be collected by frontopolar scalp Electroencephalographic (EEG) sensors. Normally, EEG recording systems meant for clinical applications are expensive and cannot be used in large-scale user-friendly applications. Thanks to a prototype made by the STMicroelectronics company, based on an Open Source EEG project, a low-cost EEG recording system was created in this work. The goal is to develop an algorithm that can detect and discriminate between voluntary (forced) and involuntary (natural) blinking so that, in the future, an EEG-based BCI system that is able to control a device through eye movements could be developed, which would be of great use for all people with motor disabilities who can control eye movements. The proposed algorithm is based on a one-dimensional (1D) Convolutional Neural Network (CNN) architecture. Frontopolar EEG signals were collected during the execution of voluntary and spontaneous blinks by four healthy subjects. A dataset of EEG epochs of including blinks was constructed and used to train and validate the proposed CNN. The proposed system allowed to discriminate the blinks performed by the subjects (voluntary vs. involuntary) with an average accuracy of 97.92%.