Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification

The non-stationarity of EEG signals results in variability across sessions, impeding model building and data sharing. In this paper, we propose a domain adaptation method called GPL, which simultaneously considers global knowledge and prototype-based local class information to enhance the classifica...

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Veröffentlicht in:	Neural networks 2024-11, Vol.179, p.106497, Article 106497
Hauptverfasser:	Zhang, Dongxue, Li, Huiying, Xie, Jingmeng
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Brain - physiology Brain-Computer Interfaces Brain–computer Interface (BCI) Deep Learning (DL) Domain adaptation (DA) Electroencephalography (EEG) Electroencephalography - methods Humans Imagination - physiology Motor Imagery (MI) Neural Networks, Computer Supervised Machine Learning Unsupervised Machine Learning
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