EEG-based stress recognition using deep learning techniques

Successful implementation of deep learning technique for electroencephalography-based stress recognition has not been accomplished yet regardless of the efficacious application of deep learning in the brain-computer interface systems. This paper proposes utilising a Convolutional Neural Network to d...

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Main Author: Nur Irsalina Zainudin
Other Authors: Wang Lipo
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/140337
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1403372023-07-07T18:50:43Z EEG-based stress recognition using deep learning techniques Nur Irsalina Zainudin Wang Lipo School of Electrical and Electronic Engineering Fraunhofer Singapore ELPWang@ntu.edu.sg Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Successful implementation of deep learning technique for electroencephalography-based stress recognition has not been accomplished yet regardless of the efficacious application of deep learning in the brain-computer interface systems. This paper proposes utilising a Convolutional Neural Network to detect stress levels. To provide a baseline so as to compare the performance between the classifiers, a Support Vector Machine, a commonly used supervised machine learning technique to detect stress has additionally been adopted in this paper. The ramifications of two different input electroencephalography representations was also further explored. The highest classification accuracies attained using the Support Vector Machine are 83.7% and 82.7% for the separate input representations, detecting two classes of stress. This is a great improvement in outcomes as compared to other similar studies. However, for the Convolutional Neural Network, an average accuracy of 50% was achieved in detecting the two classes of stress. Regardless, this project may shed light in additional methods that may be adopted to detect stress successfully using a Convolutional Neural Network. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-28T03:59:44Z 2020-05-28T03:59:44Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/140337 en A3266 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
spellingShingle Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Nur Irsalina Zainudin
EEG-based stress recognition using deep learning techniques
description Successful implementation of deep learning technique for electroencephalography-based stress recognition has not been accomplished yet regardless of the efficacious application of deep learning in the brain-computer interface systems. This paper proposes utilising a Convolutional Neural Network to detect stress levels. To provide a baseline so as to compare the performance between the classifiers, a Support Vector Machine, a commonly used supervised machine learning technique to detect stress has additionally been adopted in this paper. The ramifications of two different input electroencephalography representations was also further explored. The highest classification accuracies attained using the Support Vector Machine are 83.7% and 82.7% for the separate input representations, detecting two classes of stress. This is a great improvement in outcomes as compared to other similar studies. However, for the Convolutional Neural Network, an average accuracy of 50% was achieved in detecting the two classes of stress. Regardless, this project may shed light in additional methods that may be adopted to detect stress successfully using a Convolutional Neural Network.
author2 Wang Lipo
author_facet Wang Lipo
Nur Irsalina Zainudin
format Final Year Project
author Nur Irsalina Zainudin
author_sort Nur Irsalina Zainudin
title EEG-based stress recognition using deep learning techniques
title_short 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_sort eeg-based stress recognition using deep learning techniques
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/140337
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