Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review

The brain–computer interface (BCI) is an emerging technology that has the potential to revolutionize the world, with numerous applications ranging from healthcare to human augmentation. Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms that have been used extensive...

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Bibliographic Details
Published in:	Neural computing & applications Vol. 35; no. 20; pp. 14681 - 14722
Main Authors:	Altaheri, Hamdi, Muhammad, Ghulam, Alsulaiman, Mansour, Amin, Syed Umar, Altuwaijri, Ghadir Ali, Abdul, Wadood, Bencherif, Mohamed A., Faisal, Mohammed
Format:	Journal Article
Language:	English
Published:	London Springer London 01.07.2023 Springer Nature B.V
Subjects:	Artificial Intelligence Classification Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Datasets Deep learning Electroencephalography Health care Human-computer interface Image Processing and Computer Vision Imagery New technology Performance evaluation Preprocessing Probability and Statistics in Computer Science Rehabilitation S.I.: Machine Learning for Big Data Analytics in Smart Healthcare Systems Service robots Special Issue on Machine Learning for Big Data Analytics in Smart Healthcare Systems Deep learning Motor imagery (MI) Survey Electroencephalogram (EEG) Classification Brain–computer interface (BCI)
ISSN:	0941-0643, 1433-3058
Online Access:	Get full text
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