基于流形保持的聚类集成方法，提升脑电分类准确率
MPEC: Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers
- 用协方差矩阵与RBF核融合特征，捕捉脑电信号线性与非线性关系
- 在黎曼流形空间改进K-means，增强局部几何敏感性，提升分类精度
- 集成多个聚类分类器，在BCI竞赛数据集上表现显著优于现有方法
脑电信号分类对脑机接口和神经假体应用至关重要，但许多现有方法未考虑脑电信号的非欧几里得流形结构，导致性能不佳。保留这一流形信息对于捕捉脑电信号的真实几何特性至关重要，而传统分类技术大多忽略此需求。为此，我们提出MPEC（基于聚类集成的流形保持脑电分类），包含两项关键创新：(1) 特征工程阶段结合协方差矩阵与径向基函数（RBF）核，以同时捕捉脑电通道间的线性与非线性关系；(2) 聚类阶段采用针对黎曼流形空间优化的改进K-means算法，确保局部几何敏感性。通过集成多个基于聚类的分类器，MPEC在BCI Competition IV数据集2a上实现显著性能提升。
原文摘要 · Abstract (English)
Accurate classification of EEG signals is crucial for brain-computer interfaces (BCIs) and neuroprosthetic applications, yet many existing methods fail to account for the non-Euclidean, manifold structure of EEG data, resulting in suboptimal performance. Preserving this manifold information is essential to capture the true geometry of EEG signals, but traditional classification techniques largely overlook this need. To this end, we propose MPEC (Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers), that introduces two key innovations: (1) a feature engineering phase that combines covariance matrices and Radial Basis Function (RBF) kernels to capture both linear and non-linear relationships among EEG channels, and (2) a clustering phase that employs a modified K-means algorithm tailored for the Riemannian manifold space, ensuring local geometric sensitivity. Ensembling multiple clustering-based classifiers, MPEC achieves superior results, validated by significant improvements on the BCI Competition IV dataset 2a.
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