Motor imagery EEG signal classification using novel deep learning algorithm

Electroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurological disorder detection and cognitive state monitoring. However, these technologies face challenges and exhibit reduced performances due to signal noi...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 24539 - 24
Hauptverfasser:	Mathiyazhagan, Sathish, Devasena, M. S. Geetha
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 08.07.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/166 692/308 692/700 Accuracy Adaptive deep belief network Algorithms Brain-Computer Interfaces Brain–computer interface Classification Datasets Deep Learning Discriminant analysis EEG EEG signal processing Electroencephalography Electroencephalography - methods Far and near optimization Feature selection Fourier transforms Geometry Humanities and Social Sciences Humans Hybrid preprocessing model Imagination - physiology Mental task performance Motor imagery multidisciplinary Neural networks Optimization Real time Science Science (multidisciplinary) Signal Processing, Computer-Assisted Spatial analysis Support vector machines Wavelet Analysis Wavelet transforms Motor imagery Hybrid preprocessing model Adaptive deep belief network Far and near optimization EEG signal processing Brain–computer interface
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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