Huang, J, Yang, P, Bang, X et al. (3 more authors) (2022) Latency Aligning Task-Related Component Analysis Using Wave Propagation for Enhancing SSVEP-Based BCIs. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30. pp. 851-859. ISSN 1534-4320
Abstract
Due to the high robustness to artifacts, steady-state visual evoked potential (SSVEP) has been widely applied to construct high-speed brain-computer interfaces (BCIs). Thus far, many spatial filtering methods have been proposed to enhance the target identification performance for SSVEP-based BCIs, and task-related component analysis (TRCA) is among the most effective ones. In this paper, we further extend TRCA and propose a new method called Latency Aligning TRCA (LA-TRCA), which aligns visual latencies on channels to obtain accurate phase information from task-related signals. Based on the SSVEP wave propagation theory, SSVEP spreads from posterior occipital areas over the cortex with a fixed phase velocity. Via estimation of the phase velocity using phase shifts of channels, the visual latencies on different channels can be determined for inter-channel alignment. TRCA is then applied to aligned data epochs for target recognition. For the validation purpose, the classification performance comparison between the proposed LA-TRCA and TRCA-based expansions were performed on two different SSVEP datasets. The experimental results illustrated that the proposed LA-TRCA method outperformed the other TRCA-based expansions, which thus demonstrated the effectiveness of the proposed approach for enhancing the SSVEP detection performance.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. |
Keywords: | Brain-computer interfaces (BCIs), latency aligning task-related component analysis (LA-TRCA), phase velocity, steady-state visual evoked potential (SSVEP), wave propagation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Mar 2022 10:33 |
Last Modified: | 01 Jun 2022 10:07 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TNSRE.2022.3162029 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:185042 |