EEG-TriNet++: A Transformer-Guided Meta-Learning Framework for Robust and Generalizable Motor Imagery Classification
Motor imagery (MI) classification using EEG signals is central to brain–computer interfaces but remains challenging due to low signal-to-noise ratio, non-stationarity, and high inter-subject variability. We introduce EEG-TriNet++, a multi-branch deep learning architecture that enhances both classifi...
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| Hlavní autoři: | , , , , , |
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| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
MDPI AG
2026-03-01
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| Edice: | Bioengineering |
| Témata: | |
| On-line přístup: | https://www.mdpi.com/2306-5354/13/3/307 |
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