Rubio-Solis, A., Beltran-Perez, C. and Wei, H. orcid.org/0000-0002-4704-7346 (2022) Classification of EEG signals for brain-computer interfaces using a Bayesian-Fuzzy Extreme Learning Machine. In: Jiang, R., Zhang, L., Wei, H.L., Crookes, D. and Chazot, P., (eds.) Recent Advances in AI‑enabled Automated Medical Diagnosis. AI4MED 2021 : 2021 International Symposium on Artificial Intelligence for Medical Applications, 19-23 Aug 2021, Virtual Conference (Newcastle upon Tyne, UK). Taylor & Francis , pp. 347-362. ISBN 9781032008431
Abstract
In brain-computer interface (BCI) applications, classification of motor imagery electroencephalogram (EEG) using Extreme Learning Machine (ELM) theory dates back to 2006. Even though, it is relatively new, advances in ELM-based classification have demonstrated to be a robust method-ology with strong generalization properties. In this study, a unified framework based on Bayesian and Fuzzy ELM theory referred to as Bayesian-Fuzzy Extreme Learning Machine (BFELM) is developed for EEG signals classification. The proposed methodology is a hybrid approach for the training of a class of Fuzzy Inference Systems (FISs) of Takagi-Sugeno-Kang (TSK). On the one hand, Fuzzy logic theory is applied to handle any bounded non-constant piecewise continuous membership functions (MFs). On the other hand, Bayesian ELM theory is used to calculate the consequent parameters of each fuzzy rule by estimating their likelihood while minimizing training error and improving associated model generalization. Performance comparison of BFELM with other existing ELM methods and Support Vector Machine (SVM) is implemented for the classification of EEG signals using two public data sets. The experimental results confirm the advantages of using a unified framework for an improved classification of EEG data associated with motor imagery (MI) in BCI applications.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. This is an author-produced version of a paper subsequently published in Recent Advances in AI-enabled Automated Medical Diagnosis. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Nov 2021 08:00 |
Last Modified: | 20 Oct 2023 00:13 |
Status: | Published |
Publisher: | Taylor & Francis |
Refereed: | Yes |
Identification Number: | 10.1201/9781003176121-22 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179760 |