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A measurement of motor recovery for motor imagery-based BCI using EEG coherence analysis.

, , , , , , , , , и . ICICS, стр. 1-5. IEEE, (2015)

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William Chew

Wakene Negassa Chewaka

Chew-Wun Wu

Chew-Yean Yam

 

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Triaxial rehabilitative data analysis incorporating matching pursuit., , , , и . EUSIPCO, стр. 434-438. IEEE, (2017)Transcranial direct current stimulation and EEG-based motor imagery BCI for upper limb stroke rehabilitation., , , , , , и . EMBC, стр. 4128-4131. IEEE, (2012)Surrogate Rehabilitative Time Series Data for Image-based Deep Learning., , , , , и . EUSIPCO, стр. 1-5. IEEE, (2019)Assessment of the Efficacy of EEG-Based MI-BCI With Visual Feedback and EEG Correlates of Mental Fatigue for Upper-Limb Stroke Rehabilitation., , , , , , , , , и 2 other автор(ы). IEEE Trans. Biomed. Eng., 67 (3): 786-795 (2020)Mechanical design of a portable knee-ankle-foot robot., , , , , , , и . ICRA, стр. 2183-2188. IEEE, (2013)Biomechanical effects of robot assisted walking on knee joint kinematics and muscle activation pattern., , , , , и . ICORR, стр. 252-257. IEEE, (2017)Objectifying Rehabilitative Tests with Singular Spectrum Analysis., , , , , , и . ICMSSP, стр. 10-15. IEEE, (2016)Neural Network Control of a Rehabilitation Robot by State and Output Feedback., , , , и . J. Intell. Robotic Syst., 80 (1): 15-31 (2015)Motor imagery BCI for upper limb stroke rehabilitation: An evaluation of the EEG recordings using coherence analysis., , , , , , , и . EMBC, стр. 261-264. IEEE, (2013)Surrogate Data for Deep Learning Architectures in Rehabilitative Edge Systems., , , , , и . SPA, стр. 30-35. IEEE, (2020)