Zhang, Y, Xie, SQ orcid.org/0000-0002-8082-9112, Li, Z et al. (3 more authors) (2022) CCA-based Spatio-temporal Filtering for Enhancing SSVEP Detection. In: 2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN). 2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN), 27-30 Sep 2022, Ioannina, Greece. IEEE ISBN 9781665487917
Abstract
Brain-computer interface (BCI) can provide a direct communication path between the human brain and an external device. The steady-state visual evoked potential (SSVEP)-based BCI has been widely explored in the past decades due to its high signal-to-noise ratio and fast communication rate. Several spatial filtering methods have been developed for frequency detection. However the temporal knowledge contained in the SSVEP signal is not effectively utilized. In this study, we propose a canonical correlation analysis (CCA)-based spatio-temporal filtering method to improve target classification. The training signal and two types of template signals (i.e. individual template and artificial sine-cosine reference) are first augmented via temporal information. Three sets of augmented data are then concatenated by trials. The CCA is performed twice, between the newly obtained training data and each template. The trained four spatial filters can be applied in the following test process. A public benchmark dataset was used to evaluate the performance of the proposed method and the other three comparing methods, such as CCA, MsetCCA, and TRCA. The experimental results indicate that the proposed method yields significantly higher performance. This paper also explored the effects of the number of electrodes and training blocks on classification accuracy. The results further demonstrated the effectiveness of the proposed method in SSVEP detection.
Metadata
Item Type: | Proceedings Paper |
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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) , steady-state visual evoked potential (SSVEP) , data augmentation |
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) The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Biomedical Sciences (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/S019219/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Jan 2023 13:42 |
Last Modified: | 27 Jan 2023 13:42 |
Published Version: | http://dx.doi.org/10.1109/bsn56160.2022.9928502 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/bsn56160.2022.9928502 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:195717 |