KongFatt Wong‐Lin
Papers
6
Total Citations
78
H-Index
4
About
KongFatt Wong-Lin is a pioneering researcher at the intersection of computational neuroscience, neural connectivity, and bio-inspired robotics. His work primarily focuses on understanding how the brain controls movement and decision-making, with significant contributions to both rehabilitation technology and autonomous systems. Wong-Lin’s most impactful research examines directed functional connectivity in the brain during robot-assisted gait training, using partial Granger causality to reveal how fronto-centroparietal circuits correlate with motor adaptation—a finding with 44 citations that has implications for improving stroke rehabilitation. He has also developed drift-diffusion models for biological source seeking in mobile robots, addressing sensor noise challenges that bridge neuroscience and robotics. His notable achievements include proposing adaptive inhibitory control mechanisms for real-time applications and designing a single-chip system for sensor data fusion based on perceptual decision-making. With over 78 citations across his key papers, Wong-Lin’s work uniquely integrates neural dynamics with engineering solutions, offering insights into how biological principles can enhance robotic systems and therapeutic interventions. His research is essential reading for those interested in neural control, rehabilitation engineering, and bio-inspired robotics.
Research Focus
Key Achievements
Top Papers
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- 3A drift diffusion model of biological source seeking for mobile robots10 citations · 2017
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- 6Stability Analysis of Bio-inspired Source Seeking with Noisy Sensors2 citations · 2021