Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review-Reference-Cited by-同舟云学术

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

Published:2021-08-25 Issue: Volume: Page:
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Altaheri Hamdi,Muhammad Ghulam^ORCID,Alsulaiman Mansour,Amin Syed Umar,Altuwaijri Ghadir Ali,Abdul Wadood,Bencherif Mohamed A.,Faisal Mohammed

Funder

deputyship for research & innovation, ministry of education in saudi arabia

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-021-06352-5.pdf

Reference170 articles.

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2. Masud M, Gaba GS, Alqahtani S, Muhammad G, Gupta BB, Kumar P, Ghoneim A (2020) A lightweight and robust secure key establishment protocol for internet of medical things in COVID-19 patients care. IEEE Inter Things J

3. Muhammad G, Alshehri F, Karray F, El Saddik A., Alsulaiman M, Falk TH (2021). A comprehensive survey on multimodal medical signals fusion for smart healthcare systems. Inf Fusion 76:355–375

4. Cantillo-Negrete J, Carino-Escobar RI, Carrillo-Mora P, Elias-Vinas D, Gutierrez-Martinez J (2018) Motor imagery-based brain-computer interface coupled to a robotic hand orthosis aimed for neurorehabilitation of stroke patients. J Healthc Eng 2018:1–10

5. López-Larraz E, Sarasola-Sanz A, Irastorza-Landa N, Birbaumer N, Ramos-Murguialday A (2018) Brain-machine interfaces for rehabilitation in stroke: a review. NeuroRehabilitation 43(1):77–97

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