Transferring Spatial Filters via Tangent Space Alignment in Motor Imagery BCIs
This work addresses subject transfer issues in motor imagery BCIs, but it is incremental as it builds on existing CSP methods with minor enhancements.
The paper tackled the problem of subject transfer in motor imagery BCIs by aligning covariance matrices on a Riemannian manifold and computing new spatial filters, resulting in marginal improvements over standard CSP across three datasets, with more significant gains when training data were limited.
We propose a method to improve subject transfer in motor imagery BCIs by aligning covariance matrices on a Riemannian manifold, followed by computing a new common spatial patterns (CSP) based spatial filter. We explore various ways to integrate information from multiple subjects and show improved performance compared to standard CSP. Across three datasets, our method shows marginal improvements over standard CSP; however, when training data are limited, the improvements become more significant.