DBTSP-net: A temporal-spatial parallel network with optuna optimization for subject-specific motor imagery EEG decoding and visualization

The accuracy and stability of decoding EEG-based motor imagery (MI-EEG) is critical for achieving effective human-machine interaction and promoting motor function recovery in patients with severe motor dysfunction. In this paper, we propose a novel dual-branch temporal–spatial parallel hybrid classi...

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Vydané v:	Neurocomputing (Amsterdam) Ročník 660; s. 131858
Hlavní autori:	Chen, Xin, Yu, Longjie, Lin, Hongze, Du, Mingyu, Wei, Wei, Wu, Xinyu, Bao, Guanjun, Cai, Shibo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 07.01.2026
Predmet:	Adaptive weighted feature fusion Brain-computer interface (BCI) Electroencephalogram (EEG) Motor imagery (MI) Temporal–Spatial parallel network Transformer Motor imagery (MI) Brain-computer interface (BCI) Temporal–Spatial parallel network Transformer Electroencephalogram (EEG) Adaptive weighted feature fusion
ISSN:	0925-2312
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