EEG Reconstruction With a Dual-Scale CNN-LSTM Model for Deep Artifact Removal

Artifact removal has been an open critical issue for decades in tasks centering on EEG analysis. Recent deep learning methods mark a leap forward from the conventional signal processing routines; however, those in general still suffer from insufficient capabilities 1) to capture potential temporal d...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics 2023-03, Vol.27 (3), p.1283-1294
Hauptverfasser:	Gao, Tengfei, Chen, Dan, Tang, Yunbo, Ming, Zhekai, Li, Xiaoli
Format:	Artikel
Sprache:	eng
Schlagworte:	artifact removal Artificial neural networks Brain modeling CNN Deep learning EEG Electroencephalogram (EEG) Electroencephalography Electromyography Electrooculography end-to-end Feature extraction Long short-term memory LSTM Machine learning Morphology Neural networks Reconstruction Signal processing Training Waveforms
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