Improving CCA Algorithms on SSVEP Classification with Reinforcement Learning Based Temporal Filtering

Ou, L; Do, T; Tran, XT; Leong, D; Chang, Y; Wang, Y; Lin, C

Improving CCA Algorithms on SSVEP Classification with Reinforcement Learning Based Temporal Filtering

Ou, L Do, T Tran, XT Leong, D Chang, Y

Wang, Y

Lin, C

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Publisher:: SPRINGER-VERLAG SINGAPORE PTE LTD
Publication Type:: Chapter
Citation:: AI 2023: Advances in Artificial Intelligence, 2024, 14472 LNAI, pp. 376-386
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Ou, L
dc.contributor.author	Do, T
dc.contributor.author	Tran, XT
dc.contributor.author	Leong, D
dc.contributor.author	Chang, Y https://orcid.org/0000-0001-9244-0318
dc.contributor.author	Wang, Y https://orcid.org/0000-0001-8390-2664
dc.contributor.author	Lin, C https://orcid.org/0000-0001-8371-8197
dc.contributor.editor	Liu, T
dc.contributor.editor	Yue, L
dc.contributor.editor	Webb, G
dc.contributor.editor	Wang, D
dc.date.accessioned	2024-03-31T15:38:30Z
dc.date.available	2024-03-31T15:38:30Z
dc.date.issued	2024-01-01
dc.identifier.citation	AI 2023: Advances in Artificial Intelligence, 2024, 14472 LNAI, pp. 376-386
dc.identifier.isbn	9789819983902
dc.identifier.uri	http://hdl.handle.net/10453/177377
dc.description.abstract	Canonical Correlation Analysis (CCA) has been widely used in Steady-State Visually Evoked Potential (SSVEP) analysis, but there are still challenges in this research area, specifically regarding data quality and insufficiency. In contrast to most previous studies that primarily concentrate on the development of spatial or spectral templates for SSVEP data, this paper proposes a novel temporal filtering method based on a reinforcement learning (RL) algorithm for CCA on SSVEP data. The proposed method leverages RL to automatically and precisely detect and filter low-quality segments in the SSVEP data, thereby improving the accuracy of CCA. Additionally, the proposed RL-based Temporal Filtering is algorithm-independent and compatible with various CCA algorithms. The RL-based Temporal Filtering is evaluated using a wearable dataset consisting of 102 subjects. The experimental results demonstrate significant advancements in CCA accuracy, particularly when combined with the extended CCA (ECCA) algorithm. In addition to performance enhancement, the RL-based Temporal Filtering method provides visualizable filters, which can ensure the transparency of the filtering process and the reliability of the obtained results. By addressing data quality and insufficiency concerns, this novel RL-based Temporal Filtering approach demonstrates promise in advancing SSVEP analysis for various applications.
dc.language	en
dc.publisher	SPRINGER-VERLAG SINGAPORE PTE LTD
dc.relation.ispartof	AI 2023: Advances in Artificial Intelligence
dc.relation.ispartofseries	Lecture Notes in Artificial Intelligence
dc.relation.isbasedon	10.1007/978-981-99-8391-9_30
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.title	Improving CCA Algorithms on SSVEP Classification with Reinforcement Learning Based Temporal Filtering
dc.type	Chapter
utslib.citation.volume	14472 LNAI
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	embargoed	*
utslib.copyright.embargo	2025-11-27T00:00:00+1000Z
dc.date.updated	2024-03-31T15:38:29Z
pubs.publication-status	Published
pubs.volume	14472 LNAI

Abstract:

Canonical Correlation Analysis (CCA) has been widely used in Steady-State Visually Evoked Potential (SSVEP) analysis, but there are still challenges in this research area, specifically regarding data quality and insufficiency. In contrast to most previous studies that primarily concentrate on the development of spatial or spectral templates for SSVEP data, this paper proposes a novel temporal filtering method based on a reinforcement learning (RL) algorithm for CCA on SSVEP data. The proposed method leverages RL to automatically and precisely detect and filter low-quality segments in the SSVEP data, thereby improving the accuracy of CCA. Additionally, the proposed RL-based Temporal Filtering is algorithm-independent and compatible with various CCA algorithms. The RL-based Temporal Filtering is evaluated using a wearable dataset consisting of 102 subjects. The experimental results demonstrate significant advancements in CCA accuracy, particularly when combined with the extended CCA (ECCA) algorithm. In addition to performance enhancement, the RL-based Temporal Filtering method provides visualizable filters, which can ensure the transparency of the filtering process and the reliability of the obtained results. By addressing data quality and insufficiency concerns, this novel RL-based Temporal Filtering approach demonstrates promise in advancing SSVEP analysis for various applications.

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http://hdl.handle.net/10453/177377