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

--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. Gener-alisable 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 T angent Space Alignment (ITSA), a novel pre-alignment strategy incorporating subject-specific recentering, distribution matching, and supervised rotational alignment to enhance cross-subject generalisation. 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. Brain-computer interfaces (BCI) are effective tools for motor rehabilitation and understanding musical stimulus effects on motor function [1]-[4]. In stroke rehabilitation, BCIs decode the user's intention from brain electrical activity to provide sensorimotor feedback and enable control of external devices or motor functions [5], [6]. The use of these BCI strategies for motor rehabilitation has been grouped into either assistive or rehabilitative. The former focuses on bypassing the damaged neuronal pathways to provide alternative control of the external devices, whereas the latter aims to exploit neuro-plasticity by promoting the recovery of damaged pathways and therefore restoring impaired motor functions [5]. Electroen-cephalography signals are often used for the input of BCIs as they provide portable, non-invasive, low-cost solutions and have high temporal resolution [7].