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https://hdl.handle.net/10356/148896
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ang, Jerica | en_US |
dc.date.accessioned | 2021-05-20T13:26:44Z | - |
dc.date.available | 2021-05-20T13:26:44Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | 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 | en_US |
dc.identifier.uri | https://hdl.handle.net/10356/148896 | - |
dc.description.abstract | 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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Nanyang Technological University | en_US |
dc.subject | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence | en_US |
dc.title | EEG-based stress recognition using deep learning techniques | en_US |
dc.type | Final Year Project (FYP) | en_US |
dc.contributor.supervisor | Wang Lipo | en_US |
dc.contributor.school | School of Electrical and Electronic Engineering | en_US |
dc.description.degree | Bachelor of Engineering (Electrical and Electronic Engineering) | en_US |
dc.contributor.research | Fraunhofer Singapore | en_US |
dc.contributor.supervisoremail | ELPWang@ntu.edu.sg | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | restricted | - |
Appears in Collections: | EEE Student Reports (FYP/IA/PA/PI) |
Files in This Item:
File | Description | Size | Format | |
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FYP Final Report.pdf Restricted Access | 1.57 MB | Adobe PDF | View/Open |
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