Iranian Journal of Biomedical Engineering (IJBME)
نشریه علمی مهندسی پزشکی زیستی

SSVEP-based biometric authentication using Mset-CCA-LDA method in BCI systems

Articles in Press

Document Type : Full Research Paper

Authors

Ali Maleki
Mahla Ahmadi

Department of Biotechnology, Faculty of New Science and Technologies, Semnan University, Semnan, Iran

10.22041/ijbme.2026.2080778.2013
Abstract
Biometric authentication based on brain signals, especially in steady-state visually evoked potential (SSVEP)- based approaches, is regarded as a robust, spoof-resistant, and reliable method in security systems and brain–computer interface applications. In this study, an authentication process for SSVEP signals was performed using spatial feature extraction and classification. To this end, using the Benchmark dataset including 35 subjects, a feature-extraction framework based on multiset canonical correlation analysis (Mset-CCA) was designed and implemented to effectively extract stable and discriminative patterns of brain activity for personal identity recognition. Classification was performed using linear discriminant analysis (LDA), resulting in an accuracy of 99.43% in identity recognition. For comparison, the results obtained using several other classifiers, including support vector machine, k-nearest neighbor, ensemble learning, decision tree, and naive Bayes, were also computed and reported. The findings demonstrated that the proposed method, based on Mset-CCA and the LDA classifier, substantially improved the authentication system’s performance compared to state-of-the-art methods

Keywords

Authentication
Brain–computer interface
Steady-state visual evoked potential
LDA classifier
Multi set canonical correlation analysis
Subjects
Iranian Journal of Biomedical Engineering (IJBME)

Articles in Press, Accepted Manuscript
Available Online from 17 August 2026

  • Receive Date 13 December 2025
  • Revise Date 17 August 2026
  • Accept Date 17 August 2026

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