Predicting the tasks of disabled persons using deep learning-based motor imagery model in BCI applications
Author:
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42600-023-00321-8.pdf
Reference37 articles.
1. Amin SU, Alsulaiman M, Muhammad G, Bencherif MA, Hossain MS. Multilevel weighted feature fusion using convolutional neural networks for EEG motor imagery classification. IEEE Access. 2019;7:18940–50.
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3. Ang KK, Guan C. EEG-based strategies to detect motor imagery for control and rehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2017;25(4):392–401.
4. Attallah O, Abougharbia J, Tamazin M, Nasser AA. A BCI system based on motor imagery for assisting people with motor deficiencies in the limbs. Brain Sci. 2020;10(11):864.
5. Autthasan P, Du X, Arnin J, Lamyai S, Perera M, Itthipuripat S, Yagi T, Manoonpong P, Wilaiprasitporn T. A single-channel consumer-grade EEG device for brain–computer interface: enhancing detection of SSVEP and its amplitude modulation. IEEE Sens J. 2019;20(6):3366–78.
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