Riemannian Geometry-Based Cross-Subject Motor Imagery EEG Classification via Subject-Adaptive Prototype Selection
2026 8th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA), Ankara, Türkiye, 21 - 23 Mayıs 2026, ss.1-8, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/ichora69329.2026.11537072
- Basıldığı Şehir: Ankara
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.1-8
- Karadeniz Teknik Üniversitesi Adresli: Evet
Özet
This paper proposes a Riemannian alignment framework for cross-subject
motor imagery classification using the Leave-One-Subject-Out (LOSO)
strategy, combining manifold-based geometry with multi-band spectral
analysis and discriminability-guided band selection. The proposed
framework applies a multi-band filter-bank decomposition of five
frequency bands, selected through a systematic band reduction analysis,
to capture frequency-specific discriminative patterns. Each trial is
represented as a regularized spatial covariance matrix on the symmetric
positive-definite (SPD) manifold, and subjectadaptive prototype (SAP)
selection constructs a target-specific training pool by selecting the
geometrically closest source subjects using the Riemannian geodesic
distance. The selected source covariances are aligned to the target
subject using ManifoldConsistent Alignment (MCA). A dual-expert ensemble
combines a global wide-band geometry specialist with a multi-band
spectral specialist, and their probabilistic outputs are fused via
arithmetic mean to improve robustness across neurophysiological
profiles. The framework was evaluated on the PhysioNet EEGMMIDB dataset,
achieving a mean accuracy of 72.44% and a Cohen's Kappa of 0.450 across
the first 10 subjects without using any labeled data from the target
subject.