Trade-Off Between Real-Time and Classification Performance in Motor Imagery BCI
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-61628-0_37
Reference21 articles.
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2. Cervera, M.A., Soekadar, S.R., Ushiba, J., et al.: Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Ann. Clin. Transl. Neurol. 5, 651–663 (2018). https://doi.org/10.1002/acn3.544
3. Müller, K.-R., Krauledat, M., Dornhege, G., et al.: Machine learning techniques for brain-computer interfaces. Mach. Learn. (2004). https://doi.org/10.1017/CBO9781107415324.004
4. Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., Yger, F.: A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J. Neural Eng. 15(3), 031005 (2018). https://doi.org/10.1088/1741-2552/aab2f2
5. Zhang, M., Zhu, F., Jia, F., et al.: Efficacy of brain-computer interfaces on upper extremity motor function rehabilitation after stroke: a systematic review and meta-analysis. NeuroRehabilitation (2023). https://doi.org/10.3233/NRE-230215
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