Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI

Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI

Despite the effective application of deep learning (DL) in brain-computer interface (BCI) systems, the successful execution of this technique, especially for inter-subject classification, in cognitive BCI has not been accomplished yet. In this paper, we propose a framework based on the deep convolut...

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Main Authors: Fahimi, Fatemeh, Zhang, Zhuo, Goh, Wooi Boon, Lee, Tih-Shi, Ang, Kai Keng, Guan, Cuntai
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2020
Subjects:
Engineering::Computer science and engineering
Attention
BCI
Online Access:https://hdl.handle.net/10356/144956
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1449562020-12-07T01:33:38Z Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI Fahimi, Fatemeh Zhang, Zhuo Goh, Wooi Boon Lee, Tih-Shi Ang, Kai Keng Guan, Cuntai School of Computer Science and Engineering Institute for Infocomm Research, A*STAR Engineering::Computer science and engineering Attention BCI Despite the effective application of deep learning (DL) in brain-computer interface (BCI) systems, the successful execution of this technique, especially for inter-subject classification, in cognitive BCI has not been accomplished yet. In this paper, we propose a framework based on the deep convolutional neural network (CNN) to detect the attentive mental state from single-channel raw electroencephalography (EEG) data. Accepted version 2020-12-07T01:33:38Z 2020-12-07T01:33:38Z 2019 Journal Article Fahimi, F., Zhang, Z., Goh, W. B., Lee, T.-S., Ang, K. K., & Guan, C. (2019). Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI. Journal of Neural Engineering, 16(2), 026007-. doi:10.1088/1741-2552/aaf3f6 1741-2560 https://hdl.handle.net/10356/144956 10.1088/1741-2552/aaf3f6 30524056 2 16 en Journal of neural engineering © 2019 IOP Publishing Ltd. All rights reserved. This is an author-created, un-copyedited version of an article accepted for publication in Journal of neural engineering. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher authenticated version is available online at https://doi.org/10.1088/1741-2552/aaf3f6 application/pdf
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
Attention
BCI
spellingShingle Engineering::Computer science and engineering
Attention
BCI
Fahimi, Fatemeh
Zhang, Zhuo
Goh, Wooi Boon
Lee, Tih-Shi
Ang, Kai Keng
Guan, Cuntai
Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI
description Despite the effective application of deep learning (DL) in brain-computer interface (BCI) systems, the successful execution of this technique, especially for inter-subject classification, in cognitive BCI has not been accomplished yet. In this paper, we propose a framework based on the deep convolutional neural network (CNN) to detect the attentive mental state from single-channel raw electroencephalography (EEG) data.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Fahimi, Fatemeh
Zhang, Zhuo
Goh, Wooi Boon
Lee, Tih-Shi
Ang, Kai Keng
Guan, Cuntai
format Article
author Fahimi, Fatemeh
Zhang, Zhuo
Goh, Wooi Boon
Lee, Tih-Shi
Ang, Kai Keng
Guan, Cuntai
author_sort Fahimi, Fatemeh
title Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI
title_short Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI
title_full Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI
title_fullStr Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI
title_full_unstemmed Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI
title_sort inter-subject transfer learning with an end-to-end deep convolutional neural network for eeg-based bci
publishDate 2020
url https://hdl.handle.net/10356/144956
_version_ 1688665662488051712

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