MPEC: Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers

ORCID: 0000 - 0003 - 0886 - 7023 Abstract -- 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 tradition al classification techniques largely overlook this need. To this end, w e propose MPEC (Manifold - Preserved EEG Classification via an Ensemble of Clus tering - 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 al gorithm 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. Keywords -- brain - computer interfaces (BCIs), EEG signal classification, ensemble modeling, clustering - based classification. EEG signal classification is essential in brain - computer interfaces (BCIs) and neuroprosthetics, where precise interpretation supports real - time control and cognitive applications. However, traditional techniques often overlook the non - Euclidean, manifold structure of EEG data, leading to suboptimal results [1] . We propose Manifold - Preserved EEG Classification via an Ensemble of Clustering - Based Classifiers (MPEC), a novel method that enhances classification accuracy by preserving the intrinsic manifold structure of EEG signals.