Please use this identifier to cite or link to this item:
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.
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.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP Final Report.pdf
  Restricted Access
1.57 MBAdobe PDFView/Open

Page view(s)

Updated on May 18, 2022


Updated on May 18, 2022

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.