Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition.

Zhang, D; Yao, L; Chen, K; Wang, S; Chang, X; Liu, Y

Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition.

Zhang, D Yao, L Chen, K Wang, S Chang, X

Liu, Y

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Trans Cybern, 2020, 50, (7), pp. 3033-3044
Issue Date:: 2020-07

Closed Access

	Filename	Description	Size
	Making_Sense_of_Spatio-Temporal_Preserving_Representations_for_EEG-Based_Human_Intention_Recognition.pdf	Published version	3.58 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, D
dc.contributor.author	Yao, L
dc.contributor.author	Chen, K
dc.contributor.author	Wang, S
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	Liu, Y
dc.date.accessioned	2023-03-31T09:59:52Z
dc.date.available	2023-03-31T09:59:52Z
dc.date.issued	2020-07
dc.identifier.citation	IEEE Trans Cybern, 2020, 50, (7), pp. 3033-3044
dc.identifier.issn	2168-2267
dc.identifier.issn	2168-2275
dc.identifier.uri	http://hdl.handle.net/10453/168973
dc.description.abstract	Brain-computer interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG)-based BCI is one of the promising solutions due to its convenient and portable instruments. Despite the extensive research of EEG in recent years, it is still challenging to interpret EEG signals effectively due to its nature of noise and difficulties in capturing the inconspicuous relations between EEG signals and specific brain activities. Most existing works either only consider EEG as chain-like sequences while neglecting complex dependencies between adjacent signals or requiring complex preprocessing. In this paper, we introduce two deep learning-based frameworks with novel spatio-temporal preserving representations of raw EEG streams to precisely identify human intentions. The two frameworks consist of both convolutional and recurrent neural networks effectively exploring the preserved spatial and temporal information in either a cascade or a parallel manner. Extensive experiments on a large scale movement intention EEG dataset (108 subjects, 3 145 160 EEG records) have demonstrated that the proposed frameworks achieve high accuracy of 98.3% and outperform a set of state-of-the-art and baseline models. The developed models are further evaluated with a real-world brain typing BCI and achieve a recognition accuracy of 93% over five instruction intentions suggesting good generalization over different kinds of intentions and BCI systems.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Trans Cybern
dc.relation.isbasedon	10.1109/TCYB.2019.2905157
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Algorithms
dc.subject.mesh	Brain
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Deep Learning
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Female
dc.subject.mesh	Humans
dc.subject.mesh	Intention
dc.subject.mesh	Male
dc.subject.mesh	Pattern Recognition, Automated
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Spatio-Temporal Analysis
dc.subject.mesh	Brain
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Intention
dc.subject.mesh	Algorithms
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Pattern Recognition, Automated
dc.subject.mesh	Female
dc.subject.mesh	Male
dc.subject.mesh	Spatio-Temporal Analysis
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Deep Learning
dc.subject.mesh	Algorithms
dc.subject.mesh	Brain
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Deep Learning
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Female
dc.subject.mesh	Humans
dc.subject.mesh	Intention
dc.subject.mesh	Male
dc.subject.mesh	Pattern Recognition, Automated
dc.subject.mesh	Signal Processing, Computer-Assisted
dc.subject.mesh	Spatio-Temporal Analysis
dc.title	Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition.
dc.type	Journal Article
utslib.citation.volume	50
utslib.location.activity	United States
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-31T09:59:50Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	50
utslib.citation.issue	7

Abstract:

Brain-computer interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG)-based BCI is one of the promising solutions due to its convenient and portable instruments. Despite the extensive research of EEG in recent years, it is still challenging to interpret EEG signals effectively due to its nature of noise and difficulties in capturing the inconspicuous relations between EEG signals and specific brain activities. Most existing works either only consider EEG as chain-like sequences while neglecting complex dependencies between adjacent signals or requiring complex preprocessing. In this paper, we introduce two deep learning-based frameworks with novel spatio-temporal preserving representations of raw EEG streams to precisely identify human intentions. The two frameworks consist of both convolutional and recurrent neural networks effectively exploring the preserved spatial and temporal information in either a cascade or a parallel manner. Extensive experiments on a large scale movement intention EEG dataset (108 subjects, 3 145 160 EEG records) have demonstrated that the proposed frameworks achieve high accuracy of 98.3% and outperform a set of state-of-the-art and baseline models. The developed models are further evaluated with a real-world brain typing BCI and achieve a recognition accuracy of 93% over five instruction intentions suggesting good generalization over different kinds of intentions and BCI systems.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/168973