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...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Thesis-Master by Coursework |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/166239 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-166239 |
---|---|
record_format |
dspace |
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 |