Learning generalised features from EEG using deep learning for a cognitive brain-computer interface

Attention can be measured by different types of cognitive tasks. Such tasks include the Stroop Test, Eriksen Flanker Test and the Psychomotor Vigilance Test (PVT). Despite all three cognitive tasks' different contents, they all require the use of visual attention. To learn the generalised repre...

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Main Author: Ang, Nigel Wei Jun
Other Authors: Guan Cuntai
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
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Online Access:https://hdl.handle.net/10356/148292
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1482922021-04-29T08:06:03Z Learning generalised features from EEG using deep learning for a cognitive brain-computer interface Ang, Nigel Wei Jun Guan Cuntai School of Computer Science and Engineering CTGuan@ntu.edu.sg Engineering::Computer science and engineering Attention can be measured by different types of cognitive tasks. Such tasks include the Stroop Test, Eriksen Flanker Test and the Psychomotor Vigilance Test (PVT). Despite all three cognitive tasks' different contents, they all require the use of visual attention. To learn the generalised representations from the Electroencephalography (EEG) data of different cognitive attention tasks, Intra-task and Inter-task attention classification experiments were conducted on three types of attention task data using SVM, EEGNet, and TSception. Via Intra-task experiments with Cross Validation, TSception has achieved significantly higher classification accuracies than other methods, with 82.48%, 88.22%, and 87.31% for Stroop, Flanker and PVT tests respectively. For the Inter-task experiments, Deep Learning methods showed superior performance over the SVM with most of the accuracy drops not being statistically significant (p>0.05). Our experiments indicate that there is common hidden knowledge that exists across data from the different cognitive attention tasks, and that Deep Learning methods can learn generalised representations of the data better than the SVM. Bachelor of Engineering (Computer Science) 2021-04-29T08:06:03Z 2021-04-29T08:06:03Z 2021 Final Year Project (FYP) Ang, N. W. J. (2021). Learning generalised features from EEG using deep learning for a cognitive brain-computer interface. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148292 https://hdl.handle.net/10356/148292 en 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::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Ang, Nigel Wei Jun
Learning generalised features from EEG using deep learning for a cognitive brain-computer interface
description Attention can be measured by different types of cognitive tasks. Such tasks include the Stroop Test, Eriksen Flanker Test and the Psychomotor Vigilance Test (PVT). Despite all three cognitive tasks' different contents, they all require the use of visual attention. To learn the generalised representations from the Electroencephalography (EEG) data of different cognitive attention tasks, Intra-task and Inter-task attention classification experiments were conducted on three types of attention task data using SVM, EEGNet, and TSception. Via Intra-task experiments with Cross Validation, TSception has achieved significantly higher classification accuracies than other methods, with 82.48%, 88.22%, and 87.31% for Stroop, Flanker and PVT tests respectively. For the Inter-task experiments, Deep Learning methods showed superior performance over the SVM with most of the accuracy drops not being statistically significant (p>0.05). Our experiments indicate that there is common hidden knowledge that exists across data from the different cognitive attention tasks, and that Deep Learning methods can learn generalised representations of the data better than the SVM.
author2 Guan Cuntai
author_facet Guan Cuntai
Ang, Nigel Wei Jun
format Final Year Project
author Ang, Nigel Wei Jun
author_sort Ang, Nigel Wei Jun
title Learning generalised features from EEG using deep learning for a cognitive brain-computer interface
title_short Learning generalised features from EEG using deep learning for a cognitive brain-computer interface
title_full Learning generalised features from EEG using deep learning for a cognitive brain-computer interface
title_fullStr Learning generalised features from EEG using deep learning for a cognitive brain-computer interface
title_full_unstemmed Learning generalised features from EEG using deep learning for a cognitive brain-computer interface
title_sort learning generalised features from eeg using deep learning for a cognitive brain-computer interface
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/148292
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