A spatial-frequency-temporal 3D convolutional neural network for motor imagery EEG signal classification

Motor imagery (MI) EEG signal classification is a critical issue for brain–computer interface (BCI) systems. In traditional MI EEG machine learning algorithms, feature extraction and classification often have different objective functions, thus resulting in information loss. To solve this problem, a...

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Bibliographic Details
Published in:	Signal, image and video processing Vol. 15; no. 8; pp. 1797 - 1804
Main Authors:	Miao, Minmin, Hu, Wenjun, Zhang, Wenbin
Format:	Journal Article
Language:	English
Published:	London Springer London 01.11.2021 Springer Nature B.V
Subjects:	Accuracy Algorithms Artificial neural networks Classification Competition Computer Imaging Computer Science Datasets Electroencephalography Feature extraction Human-computer interface Image classification Image Processing and Computer Vision Machine learning Multimedia Information Systems Original Paper Pattern Recognition and Graphics Signal classification Signal,Image and Speech Processing Three dimensional models Vision Spatial frequency temporal (SFT) Electroencephalogram (EEG) 3D convolutional neural network (3D CNN) Brain–computer interface (BCI)
ISSN:	1863-1703, 1863-1711
Online Access:	Get full text
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