Image-based EEG signal processing for driving fatigue prediction

Eric Juwei Cheng, Ku Young Young, Chin Teng Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This study proposes a EEG-based prediction system that transform the measured EEG record into an image-liked data for estimating the drowsiness level of drivers. Drowsy driving is one of the main factors to the occurrence of traffic accident. Since drivers themselves may not always immediately recognize that they are in the drowsy state, the risk of traffic accident increases while the driver is in the low vigilance state. In order to address this problem, the estimation of drowsy driving state via brain-computer interfaces (BCI) becomes a major concern in the driving safety field. This study transforms the measured EEG record into a image-liked feature maps, and then passes these feature maps to a Convolutional Neural Network (CNN) to learn the discriminative representations. The proposed drowsiness prediction system is evaluated by leave-one-subject-out cross-validation. The results indicate that our approach provides impressive and robust prediction performance on the EEG dataset without artifact removal process.

Original languageEnglish
Title of host publication2018 International Automatic Control Conference, CACS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538662786
DOIs
StatePublished - 9 Jan 2019
Event2018 International Automatic Control Conference, CACS 2018 - Taoyuan, Taiwan
Duration: 4 Nov 20187 Nov 2018

Publication series

Name2018 International Automatic Control Conference, CACS 2018

Conference

Conference2018 International Automatic Control Conference, CACS 2018
CountryTaiwan
CityTaoyuan
Period4/11/187/11/18

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