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

arXiv: https://arxiv.org/abs/2512.21170 · HTML: https://mlsanity.com/paper/2512.21170/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 44 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Builds on (most cited first, 5 of 5)

- [Eigenvalue and Generalized Eigenvalue Problems: Tutorial](https://mlsanity.com/paper/1903.11240/) (2019)
- [Intuitionistic Fuzzy Generalized Eigenvalue Proximal Support Vector Machine](https://mlsanity.com/paper/2408.01713/) (2024)
- [Robust Universum Twin Support Vector Machine for Imbalanced Data](https://mlsanity.com/paper/2410.20335/) (2024)
- [Granular Ball Twin Support Vector Machine with Universum Data](https://mlsanity.com/paper/2412.03375/) (2024)
- [Granular Ball K-Class Twin Support Vector Classifier](https://mlsanity.com/paper/2412.05438/) (2024)

## Abstract (arXiv)

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.