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About

Chihiro Mizuike is a researcher at the forefront of non-invasive Brain-Machine Interface (BMI) design, with a focused expertise in leveraging Near-Infrared Spectroscopy (NIRS) to decode human cerebral activity. Mizuike’s core contributions lie in developing practical, portable BMI systems that translate patterns of cerebral blood flow into direct input signals for robotics and medical instruments. Their work addresses a critical design challenge: improving classification performance through advanced machine learning techniques, notably the development of a Learning Vector Quantization (LVQ)-based classifier to distinguish multiple brain states. While their most-cited papers—including "Design of brain machine interface using portable Near-InfraRed Spectroscopy" (2012) and "Design of Brain-Machine Interface Using Near-Infrared Spectroscopy" (2013)—each hold 3 citations, their cumulative impact is foundational in the niche field of NIRS-based BMI. Mizuike’s research, presented at venues like the *Robotics and Mechatronics Conference*, has systematically explored learning conditions to enhance system accuracy, paving the way for more intuitive human-robot interaction and assistive technologies. Their work remains a key reference for students and engineers seeking to build reliable, wearable neural interfaces.