Abstract:A closed-loop brain-machine interface for elbow assistance is proposed in this work. The system decodes flexion and extension movements from noninvasive electroencephalographic signals through Kalman filter and uses them to activate a virtual device. A two-degree-of-freedom control scheme drives the model through a decoded path by generating a set of estimated inputs using differential flatness. These inputs are compensated by a feedback loop when decoding errors or external forces actuate the model. The resul… Show more
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