Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/75408
Title: EEG-based emotional stress recognition using deep learning techniques
Authors: Lai, Michelle Wei Ting
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Issue Date: 2018
Abstract: In this fast-paced world, one must be able to multi-task with jobs in hand and this generates stress. Stress come in different forms and everyone experiences it differently. Stress may not be bad as stress can be the motivation factor of pushing us to get work done daily. However, negative stress can cause mental and physical distress, causing serious health problems. Therefore, research has been done to measure stress with a stress recognition system so that the risk of stress causing health problems can be reduced. In this study, we will be using Electroencephalograph (EEG) as an indicator to evaluate stress. This indicator has been used in many previous research studies by scientists and researchers. It has also been proven that EEG can be used to identify stress due to the significant correlation between stress and EEG power. There have been many different types of EEG classifiers used in the past studies. However, not much research has been done in using convolutional neural networks (CNN) to classify EEG signals. In this study, we will be investigating if using a deep learning model as an emotion stress classifier be effective. We will be comparing the accuracies of Support Vector Machine (SVM) subject dependent and CNN subject dependent. We will also be comparing CNN subject dependent and CNN subject independent methods.
URI: http://hdl.handle.net/10356/75408
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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