Mika Mochida

Papers

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About

Mika Mochida is a researcher at the forefront of Brain–Machine Interfaces (BMIs) and assistive robotics, with a specialized focus on augmenting human motor performance. Her work centers on decoding neural signals to control wearable exoskeletons, particularly for the upper limbs, aiming to enhance the physical capabilities of healthy individuals. Mochida’s most cited paper, “EEG-Based EMG Estimation of Shoulder Joint for the Power Augmentation System of Upper Limbs” (2020, 10 citations), introduces a novel method for estimating muscle activity from electroencephalography (EEG) signals to drive power augmentation systems. Her earlier study, “Motion Estimation for the Control of Upper Limb Wearable Exoskeleton Robot with Electroencephalography Signals” (2018, 3 citations), further explores the predictive potential of EEG, capturing neural activity before actual movement to enable seamless human–robot collaboration. By leveraging the temporal advantage of EEG—signals that precede motion—Mochida’s contributions push the boundaries of non-invasive neural control, offering a promising pathway toward intuitive, real-time exoskeleton assistance. Her work not only advances assistive technology for rehabilitation but also opens new frontiers in human performance augmentation, making her a notable emerging voice in the BMI and wearable robotics community.