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Transcranial direct current stimulation and EEG-based motor imagery BCI for upper limb stroke rehabilitation.

, , , , , , and . EMBC, page 4128-4131. IEEE, (2012)

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