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Deep spatio-temporal features optimised fusion with coordinate attention mechanism for EEG lower limb pre-movement intention decoding
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DOI：10.1016/j.bbe.2025.06.004.png)
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Abstract
En 中文
Decoding pre-movement intention is crucial in developing a brain-computer interface (BCI) for neuro-rehabilitation robotic systems. However, the weak amplitude and non-smooth characteristics of EEG signals lead to the inability of existing methods to achieve the accuracy for proper applications. This study proposed a novel pre-movement intention decoding network framework to improve accuracy by extracting and optimizing the deep spatio-temporal features of EEG signals.
Keywords:
EEG signals
pre-movement intention
brain-computer interface
neuro-rehabilitation
deep spatio-temporal features
Journal
IF:
6.6
Papers:
936
Citations:
3.3K