MAE-EEG-Transformer: A transformer-based approach combining masked autoencoder and cross-individual data augmentation pre-training for EEG classification

Convolutional neural networks (CNN) may not be ideal for extracting global temporal features from non-stationary Electroencephalogram (EEG) signals. The application of the masking-based method in EEG classification is not well studied, and there is a shortage of commonly accepted models for verifyin...

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Veröffentlicht in:	Biomedical signal processing and control Jg. 94; S. 106131
Hauptverfasser:	Cai, Miao, Zeng, Yu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.08.2024
Schlagworte:	EEG Masked autoencoder Motor imagery Pre-training Transformer Pre-training Motor imagery Transformer Masked autoencoder EEG
ISSN:	1746-8094
Online-Zugang:	Volltext
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