Zhang, Y, Li, Z orcid.org/0000-0003-2583-5082, Xie, SQ orcid.org/0000-0002-8082-9112 et al. (3 more authors) (2022) Multi-Objective Optimization-Based High-Pass Spatial Filtering for SSVEP-Based Brain–Computer Interfaces. IEEE Transactions on Instrumentation and Measurement, 71. 4000509. ISSN 0018-9456
Abstract
Many spatial filtering methods have been proposed to enhance the target identification performance for the steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI). The existing approaches tend to learn spatial filter parameters of a certain target using only the training data from the same stimulus, and they rarely consider the information from other stimuli or the volume conduction problem during the training process. In this article, we propose a novel multi-objective optimization-based high-pass spatial filtering method to improve the SSVEP detection accuracy and robustness. The filters are derived via maximizing the correlation between the training signal and the individual template from the same target whilst minimizing the correlation between the signal from other targets and the template. The optimization will also be subject to the constraint that the sum of filter elements is zero. The evaluation study on two self-collected SSVEP datasets (including 12 and four frequencies, respectively) shows that the proposed method outperformed the compared methods such as canonical correlation analysis (CCA), multiset CCA (MsetCCA), sum of squared correlations (SSCOR), and task-related component analysis (TRCA). The proposed method was also verified on a public 40-class SSVEP benchmark dataset recorded from 35 subjects. The experimental results have 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: | © 2022 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. |
Keywords: | Brain–computer interface (BCI), electroencephalography (EEG), high-pass spatial filter, multi-objective optimization, steady-state visual evoked potential (SSVEP) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/S019219/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Jan 2022 09:53 |
Last Modified: | 22 Mar 2022 19:26 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TIM.2022.3146950 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182567 |