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Ontology Based Semantic Labeling Framework for EEG Cognitive State Clustering

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2026, 12(1), pp.121~126
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : January 24, 2026
  • Accepted : February 20, 2026
  • Published : February 28, 2026

Guijung Kim 1

1백석대학교

Accredited

ABSTRACT

This paper proposes an ontology-based semantic labeling framework to address the interpretability limitation of unsupervised clustering results in EEG-based cognitive state analysis. Conventional approaches mainly rely on supervised classification, which requires extensive labeled data and suffers from limited generalization across users. To overcome these limitations, the proposed framework applies unsupervised clustering to EEG feature representations and assigns semantic cognitive state labels using an ontology-based knowledge model. The framework separates data-driven analysis from knowledge-driven interpretation while integrating them at the semantic labeling stage. Experimental analysis demonstrates that clustered EEG patterns can be systematically mapped to interpretable cognitive state concepts through ontology-based rules. The proposed framework enhances the interpretability of EEG cognitive state analysis and can be flexibly applied to various IoT-based intelligent service environments.

Citation status

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