Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149462
Title: Electroencephalogram (EEG)-based fatigue recognition using deep learning techniques
Authors: Cheng, Zhiao
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Cheng, Z. (2021). Electroencephalogram (EEG)-based fatigue recognition using deep learning techniques. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149462
Project: A3279-201
Abstract: 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.
URI: https://hdl.handle.net/10356/149462
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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