An EEG-based brain-computer interface for real-time multi-task robotic control.

An, Y; Wong, JKW; Ling, SH

An EEG-based brain-computer interface for real-time multi-task robotic control.

An, Y Wong, JKW Ling, SH

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: Annu Int Conf IEEE Eng Med Biol Soc, 2023, 2023, pp. 1-4
Issue Date:: 2023-07

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Field	Value	Language
dc.contributor.author	An, Y
dc.contributor.author	Wong, JKW
dc.contributor.author	Ling, SH
dc.date.accessioned	2024-01-02T23:40:30Z
dc.date.available	2024-01-02T23:40:30Z
dc.date.issued	2023-07
dc.identifier.citation	Annu Int Conf IEEE Eng Med Biol Soc, 2023, 2023, pp. 1-4
dc.identifier.issn	2375-7477
dc.identifier.issn	2694-0604
dc.identifier.uri	http://hdl.handle.net/10453/173932
dc.description.abstract	The Brain Computer Interface (BCI) is the communication between the human brain and the computer. Electroencephalogram (EEG) is one of the biomedical signals which can be obtained by attaching electrodes to the scalp. Some EEG related applications can be developed to help disabled people, such as EEG based wheelchair or robotic arm. A hybrid BCI real-time control system is proposed to control a multi-tasks BCI robot. In this system, a sliding window based online data segmentation strategy is proposed to segment training data, which enable the system to learn the dynamic features when the subject's brain state transfer from a rest state to a task execution state. The features help the system achieve real-time control and ensure the continuity of executing actions. In addition, Common Spatial Pattern (CSP) can better extract the spatial features of these continuous actions from the dynamic data to ensure that multiple control commands are accurately classified. In the experiment, three subjects' EEG data is collected, trained and tested the performance and reliability of the proposed control system. The system records the robot's spending time, moving distance, and the number of objects pushing down. Experimental results are given to show the feasibility of the real-time control system. Compared to real-time remote controller, the proposed system can achieve similar performance. Thus, the proposed hybrid BCI real-time control system is able to control the robot in the real-time environment and can be used to develop robot-aided arm training methods based on neurological rehabilitation principles for stroke and brain injury patients.
dc.format	Print
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	Annu Int Conf IEEE Eng Med Biol Soc
dc.relation.isbasedon	10.1109/EMBC40787.2023.10340310
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.mesh	Humans
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Robotics
dc.subject.mesh	Reproducibility of Results
dc.subject.mesh	Robotic Surgical Procedures
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Reproducibility of Results
dc.subject.mesh	Robotics
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Robotic Surgical Procedures
dc.subject.mesh	Humans
dc.subject.mesh	Brain-Computer Interfaces
dc.subject.mesh	Robotics
dc.subject.mesh	Reproducibility of Results
dc.subject.mesh	Robotic Surgical Procedures
dc.subject.mesh	Electroencephalography
dc.title	An EEG-based brain-computer interface for real-time multi-task robotic control.
dc.type	Journal Article
utslib.citation.volume	2023
utslib.location.activity	United States
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
utslib.copyright.status	recently_added	*
dc.date.updated	2024-01-02T23:40:29Z
pubs.publication-status	Published
pubs.volume	2023

Abstract:

The Brain Computer Interface (BCI) is the communication between the human brain and the computer. Electroencephalogram (EEG) is one of the biomedical signals which can be obtained by attaching electrodes to the scalp. Some EEG related applications can be developed to help disabled people, such as EEG based wheelchair or robotic arm. A hybrid BCI real-time control system is proposed to control a multi-tasks BCI robot. In this system, a sliding window based online data segmentation strategy is proposed to segment training data, which enable the system to learn the dynamic features when the subject's brain state transfer from a rest state to a task execution state. The features help the system achieve real-time control and ensure the continuity of executing actions. In addition, Common Spatial Pattern (CSP) can better extract the spatial features of these continuous actions from the dynamic data to ensure that multiple control commands are accurately classified. In the experiment, three subjects' EEG data is collected, trained and tested the performance and reliability of the proposed control system. The system records the robot's spending time, moving distance, and the number of objects pushing down. Experimental results are given to show the feasibility of the real-time control system. Compared to real-time remote controller, the proposed system can achieve similar performance. Thus, the proposed hybrid BCI real-time control system is able to control the robot in the real-time environment and can be used to develop robot-aided arm training methods based on neurological rehabilitation principles for stroke and brain injury patients.

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

http://hdl.handle.net/10453/173932