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About

Fumiaki Iwane is a leading researcher at the intersection of brain-computer interfaces (BCI), assistive robotics, and neural decoding. His work focuses on enabling intuitive, human-centered control of robotic systems by directly interpreting cognitive and motor signals from the brain. A major contribution is his development of EEG-based decoding for lower-limb movement onset, achieving continuous classification and asynchronous detection—a critical step toward promoting neural plasticity and motor recovery in rehabilitation. His highly cited 2018 paper on this topic has garnered 70 citations, underscoring its impact on the field. Iwane has also pioneered the use of inverse reinforcement learning combined with error-related potentials (ErrPs) to customize assistive robotic manipulators for individuals with upper-limb disabilities, allowing robots to adapt to user preferences in real time. His work on inferring subjective preferences from EEG signals, including obstacle avoidance comfort margins, demonstrates a novel approach to personalizing robotic trajectories. Notably, his 2025 paper on on-scalp printing of personalized EEG e-tattoos introduces a groundbreaking, minimally invasive method for high-fidelity neural recording. Through these innovations, Iwane is advancing a future where assistive robots are not only controlled by thought but also learn and adapt to individual human intentions.