Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148896
Title: EEG-based stress recognition using deep learning techniques
Authors: Ang, Jerica
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2021
Publisher: Nanyang Technological University
Source: 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
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.
URI: https://hdl.handle.net/10356/148896
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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