基于泛化特征空间集成广义特征值邻近支持向量机的脑电信号癫痫发作检测统一框架 (A Unified Framework for EEG Seizure Detection Using Universum-Integrated Generalized Eigenvalues Proximal Support Vector Machine)

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.

翻译：本文提出了两种新型的泛化特征空间增强分类器：泛化特征空间广义特征值邻近支持向量机（U-GEPSVM）及其改进版本（IU-GEPSVM），用于脑电信号分类。该模型结合广义特征值分解的计算效率与泛化特征空间学习的泛化优势，旨在解决脑电分析中的关键挑战：非平稳性、低信噪比以及标记数据有限。U-GEPSVM通过基于比值的优化目标函数引入泛化特征空间约束，扩展了GEPSVM框架；而IU-GEPSVM采用基于加权差分的优化形式增强稳定性，实现对类别分离与泛化特征空间对齐的独立调控。在波恩大学脑电数据集上，针对两项二分类任务（(O vs S)-健康状态（闭眼）vs 癫痫发作、(Z vs S)-健康状态（睁眼）vs 癫痫发作）进行评估。IU-GEPSVM在两项任务中分别取得85%（O vs S）和80%（Z vs S）的峰值准确率，平均准确率分别为81.29%和77.57%，其性能优于基线方法。