Neural Netw. 2026 Jan 7;198:108520. doi: 10.1016/j.neunet.2025.108520. Online ahead of print.
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. Rigorous statistical validation confirms these improvements: Friedman tests reveal significant overall differences, pairwise Wilcoxon signed-rank tests with Bonferroni correction establish IU-GEPSVM’s superiority over all baselines, and win-tie-loss analysis demonstrates practical significance. Overall, integrating interictal Universum data yields an efficient and reliable solution for neurological diagnosis.
PMID:41538899 | DOI:10.1016/j.neunet.2025.108520
