Adaptive EEG preprocessing to mitigate electrode shift variability for robust motor imagery classification
Abstract Electrode placement variability poses a critical challenge in EEG-based motor imagery tasks, often resulting in reduced classification robustness. We present the Adaptive Channel Mixing Layer (ACML), a plug-and-play preprocessing module that dynamically adjusts input signal weights through...
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| Autors principals: | , , |
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| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
Nature Portfolio
2025-11-01
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| Col·lecció: | Scientific Reports |
| Matèries: | |
| Accés en línia: | https://doi.org/10.1038/s41598-025-24466-x |
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