Papers
6
Total Citations
169
H-Index
6
About
Farid Shiman’s research sits at the intersection of neural engineering and motor rehabilitation, where he develops brain- and muscle-driven interfaces to restore movement after stroke or injury. His most influential work, “Classification of different reaching movements from the same limb using EEG” (65 citations), tackles a core challenge in brain-computer interfaces: decoding multi-dimensional limb movements from noisy EEG signals. He has also made major contributions to myoelectric control, designing EMG-based systems that decode multi-joint kinematics for robot-aided therapy (30 citations) and continuous classification approaches for robotic exoskeletons (20 citations). A particularly innovative contribution is his work on mirror myoelectric interfaces (24 citations), which directly addresses pathological muscle synergies in hemiplegic patients by using the unaffected limb’s EMG patterns to guide rehabilitation of the impaired side. Across his body of work, Shiman has advanced the field from single-degree-of-freedom controls toward more natural, dexterous interfaces that can decode functional, multi-joint movements. His research demonstrates a clear translational vision: moving EEG and EMG decoding from laboratory classification tasks toward practical, patient-centered rehabilitation technologies.
Research Focus
Key Achievements
Top Papers
- 1Classification of different reaching movements from the same limb using EEG65 citations · 2017
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- 6Towards decoding of functional movements from the same limb using EEG12 citations · 2015