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...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/149462 |
| _version_ | 1811678991472394240 |
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| author | Cheng, Zhiao |
| author2 | Wang Lipo |
| author_facet | Wang Lipo Cheng, Zhiao |
| author_sort | Cheng, Zhiao |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:02:03Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/149462 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:02:03Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1494622023-07-07T18:15:40Z Electroencephalogram (EEG)-based fatigue recognition using deep learning techniques Cheng, Zhiao Wang Lipo School of Electrical and Electronic Engineering ELPWang@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering 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. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-05-31T09:05:51Z 2021-05-31T09:05:51Z 2021 Final Year Project (FYP) 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 https://hdl.handle.net/10356/149462 en A3279-201 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering Cheng, Zhiao Electroencephalogram (EEG)-based fatigue recognition using deep learning techniques |
| title | Electroencephalogram (EEG)-based fatigue recognition using deep learning techniques |
| title_full | Electroencephalogram (EEG)-based fatigue recognition using deep learning techniques |
| title_fullStr | Electroencephalogram (EEG)-based fatigue recognition using deep learning techniques |
| title_full_unstemmed | Electroencephalogram (EEG)-based fatigue recognition using deep learning techniques |
| title_short | Electroencephalogram (EEG)-based fatigue recognition using deep learning techniques |
| title_sort | electroencephalogram eeg based fatigue recognition using deep learning techniques |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/149462 |
| work_keys_str_mv | AT chengzhiao electroencephalogrameegbasedfatiguerecognitionusingdeeplearningtechniques |