# Transferring Spatial Filters via Tangent Space Alignment in Motor Imagery BCIs

arXiv: https://arxiv.org/abs/2504.17111 · HTML: https://mlsanity.com/paper/2504.17111/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification](https://mlsanity.com/paper/2202.02472/) (2022)
- [Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces](https://mlsanity.com/paper/1910.05878/) (2019)

## Abstract (arXiv)

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.