Cross-Subject and Cross-Montage EEG Transfer Learning via Individual Tangent Space Alignment and Spatial-Riemannian Feature Fusion

TL;DR

This paper introduces a novel transfer learning method for EEG-based BCIs that improves cross-subject generalisation by aligning individual tangent spaces and fusing spatial-Riemannian features, enhancing robustness and reducing calibration time.

Contribution

The paper proposes Individual Tangent Space Alignment (ITSA) and a hybrid feature fusion architecture, advancing cross-subject EEG transfer learning with improved accuracy and robustness.

Findings

ITSA significantly improves cross-subject EEG classification performance.

Parallel fusion of RCSP and Riemannian features outperforms sequential methods.

Robust performance across different data conditions and electrode setups.

Abstract

Personalised music-based interventions offer a powerful means of supporting motor rehabilitation by dynamically tailoring auditory stimuli to provide external timekeeping cues, modulate affective states, and stabilise gait patterns. Generalisable Brain-Computer Interfaces (BCIs) thus hold promise for adapting these interventions across individuals. However, inter-subject variability in EEG signals, further compounded by movement-induced artefacts and motor planning differences, hinders the generalisability of BCIs and results in lengthy calibration processes. We propose Individual Tangent Space Alignment (ITSA), a novel pre-alignment strategy incorporating subject-specific recentering, distribution matching, and supervised rotational alignment to enhance cross-subject generalisation. Our hybrid architecture fuses Regularised Common Spatial Patterns (RCSP) with Riemannian geometry in parallel and sequential configurations, improving class separability while maintaining the geometric structure of covariance matrices for robust statistical computation. Using leave-one-subject-out cross-validation, `ITSA' demonstrates significant performance improvements across subjects and conditions. The parallel fusion approach shows the greatest enhancement over its sequential counterpart, with robust performance maintained across varying data conditions and electrode configurations. The code will be made publicly available at the time of publication.