An SSVEP Stimuli Design using Real-time Camera View with Object Recognition

Chen, SK; Chen, CS; Wang, YK; Lin, CT

An SSVEP Stimuli Design using Real-time Camera View with Object Recognition

Chen, SK Chen, CS Wang, YK Lin, CT

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2020, 00, pp. 562-567
Issue Date:: 2020-12-01

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Field	Value	Language
dc.contributor.author	Chen, SK
dc.contributor.author	Chen, CS
dc.contributor.author	Wang, YK
dc.contributor.author	Lin, CT
dc.date	2020-12-01
dc.date.accessioned	2021-03-16T01:45:44Z
dc.date.available	2021-03-16T01:45:44Z
dc.date.issued	2020-12-01
dc.identifier.citation	2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2020, 00, pp. 562-567
dc.identifier.isbn	9781728125473
dc.identifier.uri	http://hdl.handle.net/10453/147232
dc.description.abstract	© 2020 IEEE. Most SSVEP-based stimuli BCIs are pre-defined using the white blocks. This kind of scenario lead less flexibility in the real life. To represent the flickers with the location, types and configurations of the objects in real world, this paper proposes an SSVEP-based BCI using real-time camera view with object recognition algorithm to provide intuitive BCI for users. A deep learning-based object recognition algorithm is used to calculate the location of the objects on the online camera view from a depth camera. After the bounding box of the objects is estimated, the location of the SSVEP flickers are designed to overlap on the object locations. An overlapping FFT and SVM is used to recognize the EEG signals into corresponding classes. In experimental results, the classification rate for camera view scenario is more than 94.1%. The results show that proposed SSVEP stimuli design is available to create an intuitive and reliable human machine interaction. The proposed results can be used for the users who have motor disabilities to further used to interact with assistive devices, such as: robotic arm and wheelchairs.
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/arc/DP180100670
dc.relation	http://purl.org/au-research/grants/arc/DP180100656
dc.relation	University of Technology Sydney
dc.relation	Department of Defence
dc.relation	Defence Science and Technology Group of the Department of Defence
dc.relation.ispartof	2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
dc.relation.ispartof	2020 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.relation.isbasedon	10.1109/SSCI47803.2020.9308563
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	An SSVEP Stimuli Design using Real-time Camera View with Object Recognition
dc.type	Conference Proceeding
utslib.citation.volume	00
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2021-03-16T01:45:44Z
pubs.finish-date	2020-12-04
pubs.publication-status	Published
pubs.start-date	2020-12-01
pubs.volume	00

Abstract:

© 2020 IEEE. Most SSVEP-based stimuli BCIs are pre-defined using the white blocks. This kind of scenario lead less flexibility in the real life. To represent the flickers with the location, types and configurations of the objects in real world, this paper proposes an SSVEP-based BCI using real-time camera view with object recognition algorithm to provide intuitive BCI for users. A deep learning-based object recognition algorithm is used to calculate the location of the objects on the online camera view from a depth camera. After the bounding box of the objects is estimated, the location of the SSVEP flickers are designed to overlap on the object locations. An overlapping FFT and SVM is used to recognize the EEG signals into corresponding classes. In experimental results, the classification rate for camera view scenario is more than 94.1%. The results show that proposed SSVEP stimuli design is available to create an intuitive and reliable human machine interaction. The proposed results can be used for the users who have motor disabilities to further used to interact with assistive devices, such as: robotic arm and wheelchairs.

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http://hdl.handle.net/10453/147232