Machine learning for physical motion identification using EEG signals: a comparative study of classifiers and hyperparameter tuning
Author:
Funder
Fundamental Research Grant Scheme
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12652-024-04764-4.pdf
Reference43 articles.
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2. Almulla L, Al-Naib I, Althobaiti M (2020) Hemodynamic responses during standing and sitting activities: a study toward fNIRS-BCI. Biomed Phys Eng Express 6(5):055005. https://doi.org/10.1088/2057-1976/ABA102
3. Bhattacharyya S, Cinel C, Citi L, Valeriani D, Poli R (2022) Walking improves the performance of a brain–computer interface for group decision making. Current research in neuroadaptive technology. Academic Press, London, pp 221–233. https://doi.org/10.1016/B978-0-12-821413-8.00017-8
4. Butt AM, Alsaffar H, Alshareef M, Qureshi KK (2022) AI prediction of brain signals for human gait using BCI device and FBG based sensorial platform for plantar pressure measurements. Sensors 22(8):3085. https://doi.org/10.3390/S22083085
5. Cajigas I, Davis KC, Prins NW, Gallo S, Naeem JA, Fisher L, Ivan ME, Prasad A, Jagid JR (2023) Brain–computer interface control of stepping from invasive electrocorticography upper-limb motor imagery in a patient with quadriplegia. Front Hum Neurosci. https://doi.org/10.3389/FNHUM.2022.1077416
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