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

TL;DR

<3-5 sentence high-level summary>This work introduces two Universum-enhanced GEPSVM variants, U-GEPSVM and IU-GEPSVM, to improve EEG seizure classification under non-stationarity and limited labeled data. By embedding Universum samples (interictal EEG) into a generalized eigenvalue framework, and by adopting a stable weighted-difference objective in IU-GEPSVM, the approach yields enhanced generalization and numerical stability. Empirical evaluation on Bonn EEG data demonstrates that IU-GEPSVM achieves the highest accuracies (e.g., 81.29% on O_vs_S with robust performance across wavelet features) and outperforms baseline GEPSVM, UTSVM, and I-GEPSVM with statistical significance. The results suggest that Universum-informed eigenvalue classifiers offer clinically meaningful improvements for automated seizure detection, with potential for real-time and multi-class extension.

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.

Figures (8)

• Figure 1: Geometric interpretation of U-GEPSVM showing two non-parallel hyperplanes separating positive class (blue squares), negative class (red circles), with Universum data (green triangles) refining the decision boundary in ambiguous regions.
• Figure 2: Average classification accuracy by model across O vs S and Z vs S tasks.
• Figure 3: Average accuracy achieved by each feature type across all models.
• Figure 4: Classification accuracy heatmap across model-feature combinations.
• Figure 5: Average performance radar chart