Machine learning for time series analysis

The EEG signal is a change in neuronal potential generated by the human brain during activity. Currently, machine learning and deep learning algorithms are widely used in areas such as feature extraction and pattern recognition. In this paper, we propose a deep learning algorithm based on EEGNet mod...

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Bibliographic Details
Main Author:	Wang, Ruyue
Other Authors:	Wen Bihan
Format:	Thesis-Master by Coursework
Language:	English
Published:	Nanyang Technological University 2023
Subjects:	Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Online Access:	https://hdl.handle.net/10356/166239
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-1662392023-07-04T16:20:54Z Machine learning for time series analysis Wang, Ruyue Wen Bihan School of Electrical and Electronic Engineering bihan.wen@ntu.edu.sg Engineering::Electrical and electronic engineering::Electronic systems::Signal processing The EEG signal is a change in neuronal potential generated by the human brain during activity. Currently, machine learning and deep learning algorithms are widely used in areas such as feature extraction and pattern recognition. In this paper, we propose a deep learning algorithm based on EEGNet model and compare the performance of both CNN and LSTM algorithms. The deep learning algorithm is applied to the BCI-based motion imagery classification. Master of Science (Signal Processing) 2023-04-19T05:27:50Z 2023-04-19T05:27:50Z 2023 Thesis-Master by Coursework Wang, R. (2023). Machine learning for time series analysis. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166239 https://hdl.handle.net/10356/166239 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::Electrical and electronic engineering::Electronic systems::Signal processing
spellingShingle	Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Wang, Ruyue Machine learning for time series analysis
description	The EEG signal is a change in neuronal potential generated by the human brain during activity. Currently, machine learning and deep learning algorithms are widely used in areas such as feature extraction and pattern recognition. In this paper, we propose a deep learning algorithm based on EEGNet model and compare the performance of both CNN and LSTM algorithms. The deep learning algorithm is applied to the BCI-based motion imagery classification.
author2	Wen Bihan
author_facet	Wen Bihan Wang, Ruyue
format	Thesis-Master by Coursework
author	Wang, Ruyue
author_sort	Wang, Ruyue
title	Machine learning for time series analysis
title_short	Machine learning for time series analysis
title_full	Machine learning for time series analysis
title_fullStr	Machine learning for time series analysis
title_full_unstemmed	Machine learning for time series analysis
title_sort	machine learning for time series analysis
publisher	Nanyang Technological University
publishDate	2023
url	https://hdl.handle.net/10356/166239
_version_	1772825206404939776

Machine learning for time series analysis

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