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

TL;DR

This paper introduces Universum-enhanced classifiers, U-GEPSVM and IU-GEPSVM, that improve EEG seizure detection by leveraging generalized eigenvalue decomposition and Universum learning to address non-stationarity and limited data.

Contribution

The paper proposes novel Universum-integrated classifiers for EEG analysis, combining eigenvalue decomposition with Universum learning for improved seizure detection.

Findings

IU-GEPSVM achieves up to 85% accuracy in seizure detection.

The models outperform baseline methods on the Bonn EEG dataset.

Enhanced stability and generalization in EEG classification.

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.