Huang, J, Yang, P, Wan, B et al. (1 more author) (2022) KDLPCCA-Based Projection for Feature Extraction in SSVEP-Based Brain-Computer Interfaces. Journal of Shanghai Jiaotong University (Science), 27 (2). pp. 168-175. ISSN 1007-1172
Abstract
An electroencephalogram (EEG) signal projection using kernel discriminative locality preserving canonical correlation analysis (KDLPCCA)-based correlation with steady-state visual evoked potential (SSVEP) templates for frequency recognition is presented in this paper. With KDLPCCA, not only a non-linear correlation but also local properties and discriminative information of each class sample are considered to extract temporal and frequency features of SSVEP signals. The new projected EEG features are classified with classical machine learning algorithms, namely, K-nearest neighbors (KNNs), naive Bayes, and random forest classifiers. To demonstrate the effectiveness of the proposed method, 16-channel SSVEP data corresponding to 4 frequencies collected from 5 subjects were used to evaluate the performance. Compared with the state of the art canonical correlation analysis (CCA), experimental results show significant improvements in classification accuracy and information transfer rate (ITR), achieving 100% and 240 bits/min with 0.5 s sample block. The superior performance demonstrates that this method holds the promising potential to achieve satisfactory performance for high-accuracy SSVEP-based brain-computer interfaces.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021, Shanghai Jiao Tong University and Springer-Verlag GmbH Germany, part of Springer Nature. This is an author produced version of an article published in Journal of Shanghai Jiaotong University (Science). Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | brain-computer interface; feature extraction; kernel discriminative locality preserving canonical correlation analysis (KDLPCCA); 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 Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number Royal Society IE161218 |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Jan 2022 16:04 |
Last Modified: | 28 Nov 2022 01:13 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/s12204-021-2387-0 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182015 |