Mika Mochita

About

Mika Mochita is a researcher in the field of brain-machine interfaces (BMI) and assistive robotics, with a focus on translating neural signals into practical control systems for power assistance and rehabilitation. Their most cited work, "Feature Extraction of Shoulder Joint’s Voluntary Flexion-Extension Movement Based on Electroencephalography Signals for Power Assistance" (2018, 3 citations), introduces a novel method for decoding voluntary shoulder movements from EEG signals, aiming to reduce the cost and complexity of controlling exoskeleton robots. This contribution is significant for developing more intuitive, non-invasive BMI systems that can support both disabled individuals in rehabilitation and healthy users in daily tasks. While early in their citation impact, Mochita’s work addresses a critical gap in affordable, real-time neural control, laying groundwork for future advancements in wearable assistive technologies. Their research underscores a commitment to making BMI-driven power assistance accessible and practical, with potential implications for neurorehabilitation and human augmentation.

Research Focus

Key Achievements

Top Papers

1. 1
   Feature Extraction of Shoulder Joint’s Voluntary Flexion-Extension Movement Based on Electroencephalography Signals for Power Assistance

   3 citations · 2018

Key Collaborators

Contact & Links