A Unified Framework for EEG Seizure Detection Using Universum-Integrated Generalized Eigenvalues Proximal Support Vector Machine

• URL: http://arxiv.org/abs/2512.21170v1
• Date: Wed, 24 Dec 2025 13:39:11 GMT
• Title: A Unified Framework for EEG Seizure Detection Using Universum-Integrated Generalized Eigenvalues Proximal Support Vector Machine
• Authors: Yogesh Kumar, Vrushank Ahire, M. A. Ganaie,
• Abstract summary: The paper presents novel Universum-enhanced classifiers for EEG signal classification.<n>The proposed models address critical challenges in EEG analysis: non-stationarity, low signal-to-noise ratio, and limited labeled data.<n>The models are evaluated on the Bonn University EEG dataset across two binary classification tasks.
• Score: 5.725795684434675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The paper presents novel Universum-enhanced classifiers: the Universum Generalized Eigenvalue Proximal Support Vector Machine (U-GEPSVM) and the Improved U-GEPSVM (IU-GEPSVM) for EEG signal classification. Using the computational efficiency of generalized eigenvalue decomposition and the generalization benefits of Universum learning, the proposed models address critical challenges in EEG analysis: non-stationarity, low signal-to-noise ratio, and limited labeled data. U-GEPSVM extends the GEPSVM framework by incorporating Universum constraints through a ratio-based objective function, while IU-GEPSVM enhances stability through a weighted difference-based formulation that provides independent control over class separation and Universum alignment. The models are evaluated on the Bonn University EEG dataset across two binary classification tasks: (O vs S)-healthy (eyes closed) vs seizure, and (Z vs S)-healthy (eyes open) vs seizure. IU-GEPSVM achieves peak accuracies of 85% (O vs S) and 80% (Z vs S), with mean accuracies of 81.29% and 77.57% respectively, outperforming baseline methods.

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