Transferring Spatial Filters via Tangent Space Alignment in Motor Imagery BCIs
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912343975264256 |
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| author | Gunasar, Tekin de Sa, Virginia |
| author_facet | Gunasar, Tekin de Sa, Virginia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17111 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transferring Spatial Filters via Tangent Space Alignment in Motor Imagery BCIs Gunasar, Tekin de Sa, Virginia Computer Vision and Pattern Recognition Quantitative Methods 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. |
| title | Transferring Spatial Filters via Tangent Space Alignment in Motor Imagery BCIs |
| topic | Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2504.17111 |