EEG-based fatigue recognition using deep learning techniques
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...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157409 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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 %. |
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