Shyam Diwakar
Papers
10
Total Citations
88
H-Index
6
About
Shyam Diwakar’s research lies at the intersection of neuroscience, robotics, and accessible education, focusing on brain-computer interfaces (BCI), neuro-inspired control algorithms, and low-cost robotic systems. His major contributions include developing EEG-based BCI techniques for controlling prosthetic arms, as demonstrated in his most-cited work (19 citations), and pioneering machine learning approaches to classify robotic arm movements using SVM and Naïve Bayes classifiers. Diwakar has also advanced spiking neural network models inspired by the cerebellum for pattern classification and trajectory prediction, bridging biological computation with robotic control. His work on open-source, remotely controlled robotic articulators has made robotics education more accessible, particularly in developing economies, with platforms like the Raspberry Pi-Arduino-based architecture enabling sustainable laboratory learning. Notable achievements include designing low-cost neuro-inspired robots for humanitarian purposes, such as affordable prosthetics, and creating online bio-robotics labs that promote scalable, open-hardware models. With over 80 citations across his key papers, Diwakar’s impact is evident in his ability to combine theoretical neurocomputational models with practical, low-cost implementations, making him a leading figure in democratizing robotics and BCI technology for education and assistive applications.
Research Focus
Key Achievements
Top Papers
- 1Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms19 citations · 2014
- 2Classifying Movement Articulation for Robotic Arms via Machine Learning12 citations · 2013
- 3
- 4
- 5
- 6
- 7Low cost neuro-inspired robots for sustainable laboratory education5 citations · 2016
- 8Online bio-robotics labs: Open hardware models and architecture5 citations · 2016
- 9
- 10