EEG-based fatigue recognition using deep learning techniques
Mental fatigue has been proven to have a huge impact on the safety of human society. As mental fatigue will dramatically reduce the concentration and reaction time of workers or drivers, mistakes and devastating consequences will occur. Therefore early detection of mental fatigue is an imperative s...
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| Other Authors: | |
| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/155413 |
| _version_ | 1811687366792839168 |
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| author | Zheng, Tianhu |
| author2 | Wang Lipo |
| author_facet | Wang Lipo Zheng, Tianhu |
| author_sort | Zheng, Tianhu |
| collection | NTU |
| description | Mental fatigue has been proven to have a huge impact on the safety of human
society. As mental fatigue will dramatically reduce the concentration and reaction time of workers or drivers, mistakes and devastating consequences will occur. Therefore early detection of mental fatigue is an imperative solution to decrease the disaster. With the development in bio-sensory technology, EEG which can detect the electrical signal of neural activity from the scalp has provided
valuable prospects to understand human brain activity. Through EEG signals we
can discover the hidden hints between brainwave and mental fatigue. Recent
research about deep learning has made many breakthroughs in areas like Image
Process and Natural Language Processing and achieved impressive results. This
dissertation mainly studies attention-based deep learning techniques for recognizing mental fatigue and achieved an average accuracy of more than 73%. And
proposed a single channel-based visualization technique to interpret the classification principle of the deep learning algorithm. |
| first_indexed | 2024-10-01T05:15:11Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/155413 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:15:11Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1554132023-07-04T17:43:03Z EEG-based fatigue recognition using deep learning techniques Zheng, Tianhu Wang Lipo School of Electrical and Electronic Engineering ELPWang@ntu.edu.sg Engineering::Computer science and engineering::Computer applications::Life and medical sciences Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics Mental fatigue has been proven to have a huge impact on the safety of human society. As mental fatigue will dramatically reduce the concentration and reaction time of workers or drivers, mistakes and devastating consequences will occur. Therefore early detection of mental fatigue is an imperative solution to decrease the disaster. With the development in bio-sensory technology, EEG which can detect the electrical signal of neural activity from the scalp has provided valuable prospects to understand human brain activity. Through EEG signals we can discover the hidden hints between brainwave and mental fatigue. Recent research about deep learning has made many breakthroughs in areas like Image Process and Natural Language Processing and achieved impressive results. This dissertation mainly studies attention-based deep learning techniques for recognizing mental fatigue and achieved an average accuracy of more than 73%. And proposed a single channel-based visualization technique to interpret the classification principle of the deep learning algorithm. Master of Science (Computer Control and Automation) 2022-02-23T02:08:03Z 2022-02-23T02:08:03Z 2021 Thesis-Master by Coursework Zheng, T. (2021). EEG-based fatigue recognition using deep learning techniques. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/155413 https://hdl.handle.net/10356/155413 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computer applications::Life and medical sciences Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics Zheng, Tianhu EEG-based fatigue recognition using deep learning techniques |
| title | EEG-based fatigue recognition using deep learning techniques |
| title_full | EEG-based fatigue recognition using deep learning techniques |
| title_fullStr | EEG-based fatigue recognition using deep learning techniques |
| title_full_unstemmed | EEG-based fatigue recognition using deep learning techniques |
| title_short | EEG-based fatigue recognition using deep learning techniques |
| title_sort | eeg based fatigue recognition using deep learning techniques |
| topic | Engineering::Computer science and engineering::Computer applications::Life and medical sciences Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics |
| url | https://hdl.handle.net/10356/155413 |
| work_keys_str_mv | AT zhengtianhu eegbasedfatiguerecognitionusingdeeplearningtechniques |