Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157409
Title: EEG-based fatigue recognition using deep learning techniques
Authors: Chua, Zhong Sheng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Chua, Z. S. (2022). EEG-based fatigue recognition using deep learning techniques. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157409
Abstract: Fatigued driving has always been a factor for traffic accidents, and it has prompted an interest in detecting driver’s fatigue. A variety of methods has been proposed and Electroencephalogram (EEG)-based mental state analysis is a reliable and effective way to detect fatigue. With the advancement of Deep machine learning, it has gained attention for producing better results than the standard approach. This paper proposes using a feature extraction which uses Autoregression (AR) to extract characteristics of the EEG signals and then process to into a classification algorithm which Convolution Neural Network (CNN) would be used. The results from another published paper using the same dataset will be utilized as a baseline for performance comparison. The proposed method would use a single channel baseline comparison and a leave one subject out validation to ensure that the actions performed are same. In comparison to the baseline, our proposed method has a mean classification accuracy for detecting fatigue at 69.59 %.
URI: https://hdl.handle.net/10356/157409
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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