Mohammad Kalbasi
Papers
1
Total Citations
4
H-Index
1
About
Mohammad Kalbasi is a rising researcher at the forefront of neural engineering and prosthetic innovation, with a focus on developing next-generation robotic prosthetic hands (RPHs) that restore natural hand functionality. His most-cited work, "A Hardware-Efficient EMG Decoder with an Attractor-based Neural Network for Next-Generation Hand Prostheses" (2024), tackles a critical bottleneck in assistive technology: the gap between advanced machine learning algorithms and practical, real-time hardware implementation. By designing an attractor-based neural network decoder for electromyographic (EMG) signals, Kalbasi enables more precise, intuitive finger movement decoding—moving beyond the limited on/off commands of current commercial prostheses. This hardware-efficient approach promises to make sophisticated prosthetic control accessible in low-power, embedded systems, directly impacting amputees' quality of life. With 4 citations already in its first year, his work signals strong early influence in the field. Kalbasi’s contributions bridge computational neuroscience and biomedical engineering, positioning him as a key voice in the push toward truly dexterous, user-responsive bionic hands. His research exemplifies how neural-inspired algorithms can drive tangible advances in human-machine interfaces.
Research Focus
Key Achievements
Top Papers
- 1