Electroencephalogram (EEG)-based fatigue recognition using deep learning techniques

Electroencephalogram (EEG)-based fatigue recognition using deep learning techniques

Fatigue driving is a growing hot issue that captures our eyes which results in more and more vehicle accidents threatening our safety. Electroencephalography (EEG) is the record of neurophysiological activities in human brain and is considered as one of the most popular ways of detecting drivers’ fa...

Full description

Saved in:
Bibliographic Details
Main Author: Cheng, Zhiao
Other Authors: Wang Lipo
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Electrical and electronic engineering
Online Access:https://hdl.handle.net/10356/149462
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:Fatigue driving is a growing hot issue that captures our eyes which results in more and more vehicle accidents threatening our safety. Electroencephalography (EEG) is the record of neurophysiological activities in human brain and is considered as one of the most popular ways of detecting drivers’ fatigue levels. In this paper, we proposed a compact Convolutional Neural Network (CNN) model to achieve high accuracy results and use visualization tool to discover cross-subject EEG features. From the results, we achieve a good performance of 73.75% mean accuracy which is higher than other conventional baseline methods.

Similar Items