Simplified Prediction Method for Detecting the Emergency Braking Intention Using EEG and a CNN Trained with a 2D Matrices Tensor Arrangement

Abstract

The driver’s mental state is frequently detected employing EEG signals which are usually converted into grayscale images to train a Machine Learning algorithm that classifies his mental status. This work aims to achieve a simplified and accurate method to detect the emergency braking intention employing EEG signals and a Convolutional Neural Network (CNN). Three main problems: computer resources, network accuracy, and the training time are defined to accomplish this aim. While a CNN is an efficient image-based classifier, it increases computing resources and processing time. Therefore, we solved these problems by training a CNN through a 2D matrices tensor designed to work with a very large database without transforming the EEG signals into grayscale images and running on a free cloud platform. However, we are well aware that physical fatigue while driving increases the mental load. Consequently, we measured the braking reaction time that proves an increment over time, negatively affecting the participants’ performance. The linear correlation between the target and non-target classes on the matrices tensor reveals that most emergency events can be very well-differentiated from not anomalous driving. The CCN accuracy is over 84% with just four electrodes-scalp, comparable to reported grayscale-based methods.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was funded by the National Agency for Research and Development (ANID)/Scholarship Program/Becas de Doctorado Nacional/DOCTORADO BECAS CHILE/2020 - 21201262.

Notes on contributors

Hermes J. Mora

Hermes J. Mora is a doctoral student at the Faculty of Engineering at Universidad de Concepción, Chile. He received the Electronic Engineering degree from the National University of Colombia. His research interests is focused on image and biomedical signals processing using Machine Learning algorithms.

Esteban J. Pino

Esteban J. Pino received the Electronics Engineering degree in 1997, M.Sc. degree in 2000 and D.Sc. in 2009. In 2004 he joined the Universidad de Concepcion where he currently serves as Associate Professor, teaching at both undergraduate and graduate level. His research interests include unobtrusive sensors and biomedical signal processing.