EEG-based stress recognition using deep learning techniques
Being able to recognize early signs of mental stress is crucial towards preventing detrimental physical and/or mental effects on one’s health state. Electroencephalogram (EEG)-based stress recognition has been a commonly used method due to its many advantages over other physiological signals. Howeve...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2021
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/148896 |
| _version_ | 1826123920250503168 |
|---|---|
| author | Ang, Jerica |
| author2 | Wang Lipo |
| author_facet | Wang Lipo Ang, Jerica |
| author_sort | Ang, Jerica |
| collection | NTU |
| description | Being able to recognize early signs of mental stress is crucial towards preventing detrimental physical and/or mental effects on one’s health state. Electroencephalogram (EEG)-based stress recognition has been a commonly used method due to its many advantages over other physiological signals. However, there has yet to be an optimal deep learning technique for EEG-based stress recognition despite the many studies. This paper proposes using a popular supervised machine learning technique, Support Vector Machine (SVM) to detect stress. To provide a baseline for performance comparison, the results reported from another research paper with similar feature extraction method will be used. The highest classification accuracy obtained is 65.69%, detecting two levels of stress. Hopefully, this paper may be able to contribute to the ever-important research on detecting mental stress. |
| first_indexed | 2024-10-01T06:12:32Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/148896 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:12:32Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1488962023-07-07T16:48:43Z EEG-based stress recognition using deep learning techniques Ang, Jerica Wang Lipo School of Electrical and Electronic Engineering Fraunhofer Singapore ELPWang@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Being able to recognize early signs of mental stress is crucial towards preventing detrimental physical and/or mental effects on one’s health state. Electroencephalogram (EEG)-based stress recognition has been a commonly used method due to its many advantages over other physiological signals. However, there has yet to be an optimal deep learning technique for EEG-based stress recognition despite the many studies. This paper proposes using a popular supervised machine learning technique, Support Vector Machine (SVM) to detect stress. To provide a baseline for performance comparison, the results reported from another research paper with similar feature extraction method will be used. The highest classification accuracy obtained is 65.69%, detecting two levels of stress. Hopefully, this paper may be able to contribute to the ever-important research on detecting mental stress. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-05-20T13:26:44Z 2021-05-20T13:26:44Z 2021 Final Year Project (FYP) Ang, J. (2021). EEG-based stress recognition using deep learning techniques. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148896 https://hdl.handle.net/10356/148896 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Ang, Jerica EEG-based stress recognition using deep learning techniques |
| title | EEG-based stress recognition using deep learning techniques |
| title_full | EEG-based stress recognition using deep learning techniques |
| title_fullStr | EEG-based stress recognition using deep learning techniques |
| title_full_unstemmed | EEG-based stress recognition using deep learning techniques |
| title_short | EEG-based stress recognition using deep learning techniques |
| title_sort | eeg based stress recognition using deep learning techniques |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/148896 |
| work_keys_str_mv | AT angjerica eegbasedstressrecognitionusingdeeplearningtechniques |