Biosignals-based driver’s awareness/emotion monitoring for future car design

Abstract

With the increasing demand for safer and better driving experience, driver assistance systems (DAS) are receiving more and more attention in car industry. By using sensors, there are many biosignals measurements available to recognize driver’s biosignals. With the collected biosignals data, programming can be used to analyze the situation.

There are many data sources can be used in DAS such as Electroencephalogram (EEG), Electrocardiogram (ECG), Electromyogram (EMG) and Electrooculogram (EOG) [1]. EEG has several advantages over other data sources including directly and early detection of the brain state, which means EEG provides a direct measure of vigilance.

In this project, we study the use of EEG in Driver Assistance System.

The aim of this project is to design experiments to obtain and compare the test accuracy of ELM in simulated driving and real driving. As well as to test the performance of artifact removal based on ICA in simulated driving and real driving.

The purpose of this report is to elaborate on what I have done on this project as well as my understanding of concepts and the knowledge gained while working on the project.