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AI-based Bayesian inference scheme to recognize electroencephalogram signals for smart healthcare

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Abstract

Canonical Correlation Analysis (CCA) is a popular way to analyze the underlying frequency components of an electroencephalogram (EEG) signal that contains Steady-State Visual Evoked Potentials (SSVEP). But solely itself may not be significant to detect the SSVEP frequency correctly. To improve its accuracy, several methods for processing the signal to optimize reference signals have been introduced. On the other hand, this paper is about “post-processing”, in another word, improving accuracy after performing CCA. This paper proposes a method to improve the accuracy of CCA recognition by breaking the EEG signal into several folds, using the recognition result of each fold to update a probability distribution by Bayesian Inference and select the target with the highest probability. The experiment is conducted on a publicly available dataset and the proposed method shows significant improvement in the overall recognition rate. For better communication and status monitoring of the paralyzed patient, this work can be used to improve the current application of the non-invasive Brain-Computer Interface in smart healthcare.

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Enquiries about data availability should be directed to the authors.

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Funding

This work has supported by the Xiamen University Malaysia Research Fund (XMUMRF) (Grant No: XMUMRF/2022-C9/IECE/0035). The authors are grateful to the Taif University Researchers Supporting Project Number (TURSP-2020/79), Taif University, Taif, Saudi Arabia for funding this work.

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Authors and Affiliations

  1. Information and Communication Technology Department, School of Computing and Data Science, Xiamen University Malaysia, 43900, Sepang, Malaysia

    Puah Jia Hong & Raja Majid Mehmood

  2. Department of Computer Science, National University of Modern Languages (NUML), Rawalpindi, 46000, Pakistan

    Muhammad Adeel Asghar

  3. Institute of Electrical Electronics and Computer Engineering, University of the Punjab, Lahore, Pakistan

    Amin Ullah

  4. Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Makkah, Saudi Arabia

    Mohammad Shorfuzzaman & Mehedi Masud

Authors
  1. Puah Jia Hong
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  2. Muhammad Adeel Asghar
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  3. Amin Ullah
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  4. Mohammad Shorfuzzaman
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  5. Mehedi Masud
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  6. Raja Majid Mehmood
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Correspondence to Raja Majid Mehmood.

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Appendices

Appendix

Supplementary materials

The source code is available on: https://github.com/swe1902119/Bayesian-SSVEP

TsingHua dataset is available on: http://www.thubci.com/en/?a=nr&id=76

Data

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Table 2 Classification accuracy of proposed method
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2

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Hong, P.J., Asghar, M.A., Ullah, A. et al. AI-based Bayesian inference scheme to recognize electroencephalogram signals for smart healthcare. Cluster Comput 26, 1221–1230 (2023). https://doi.org/10.1007/s10586-022-03678-0

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