Huang, J, Yang, P, Xiong, B et al. (5 more authors) (2022) Incorporating Neighboring Stimuli Data for Enhanced SSVEP-Based BCIs. IEEE Transactions on Instrumentation and Measurement, 71. 2521109. ISSN 0018-9456
Abstract
Various spatial filters have been proposed to enhance the target identification performance of steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs). The current methods only extract the target-related information from the corresponding stimulus to learn the spatial filter parameter. However, the SSVEP data from neighboring stimuli also contain frequency information of the target stimulus, which could be utilized to further improve the target identification performance. In this article, we propose a new method incorporating SSVEPs from the neighboring stimuli to strengthen the target-related frequency information. First, the spatial filter is obtained by maximizing the summation of covariances of SSVEP data corresponding to the target and its neighboring stimuli. Then the correlation features between spatially filtered templates and test data are calculated for target detection. For the performance evaluation, we implemented the offline experiment using the 40-class benchmark dataset from 35 subjects and the 12-target self-collected dataset from 11 subjects. Compared with the state-of-the-art spatial filtering methods, the proposed method showed superiority in classification accuracy and information transfer rate (ITR). The comparison results demonstrate the effectiveness of the proposed spatial filter for target identification in SSVEP-based BCIs.
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 interfaces (BCIs); neighboring stimuli; spatial filter; steady-state visual evoked potential (SSVEP); target recognition |
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) |
Funding Information: | Funder Grant number Royal Society IE161218 |
Depositing User: | Symplectic Publications |
Date Deposited: | 31 Oct 2022 13:49 |
Last Modified: | 15 May 2023 15:47 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/TIM.2022.3219497 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:192630 |