BCI based on pedal end-effector triggered through pedaling imagery to promote excitability over the feet motor area
Author:
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42600-021-00196-7.pdf
Reference43 articles.
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2. Barachant A, Bonnet S, Congedo M, Jutten C. Multiclass brain-computer interface classification by Riemannian geometry. IEEE Trans Biomed Eng. 2012;59:920–8.
3. Bastos-Filho TF. Introduction to non-invasive EEG-based brain-computer interfaces for assistive technologies. [S.I]. CRC Press; 2020.
4. Belda-Lois JM, et al. Rehabilitation of gait after stroke: a review towards a top-down approach. J Neuroeng Rehabil. 2011;8:66.
5. Blum S, Jacobsen NSJ, Bleichner MG, Debener S. A Riemannian modification of artifact subspace reconstruction for EEG artifact handling. Front Hum Neurosci. 2019; https://doi.org/10.3389/fnhum.2019.00141.
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1. EEG motor imagery classification using deep learning approaches in naïve BCI users;Biomedical Physics & Engineering Express;2023-06-23
2. A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control;Sensors;2022-08-03
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