Motoki Murakami

Papers

Total Citations

H-Index

About

Motoki Murakami is a researcher at the forefront of brain–machine interface (BMI) technology, with a primary focus on motor rehabilitation for paralysis patients. His work centers on decoding human motor intentions from electroencephalogram (EEG) signals to drive exoskeleton robots, enabling assisted movement for individuals with motor impairments. In his most-cited study, "Motion Discrimination from EEG Using Logistic Regression and Schmitt-Trigger-Type Threshold" (2015, 5 citations), Murakami developed a novel BMI system that combines logistic regression with a Schmitt-trigger threshold to accurately classify motion intentions from EEG data. This approach enhances the reliability of real-time motion discrimination, a critical step toward practical, patient-responsive rehabilitation devices. By integrating signal processing and machine learning, Murakami’s contributions address key challenges in non-invasive neural control, such as noise reduction and decision stability. His work has implications for restoring mobility in stroke survivors and spinal cord injury patients, bridging the gap between neural activity and robotic assistance. Though early in his citation impact, Murakami’s research represents a promising direction in neurorehabilitation, where precise intention detection can transform therapeutic outcomes.