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About

June Sic Kim is a leading researcher in brain-machine interfaces (BMIs) and motor neurorehabilitation, with a focus on restoring motor function for individuals with severe disabilities. Her work centers on decoding movement intentions from non-invasive neural signals—primarily magnetoencephalography (MEG)—to control robotic arms with high precision. Kim’s key contributions include characterizing kinesthetic versus visual motor imagery (KMI vs. VMI), demonstrating that proprioceptive imagination yields more robust neural signatures for BMI control (80 citations). She also pioneered the use of Long Short-Term Memory (LSTM) networks to significantly improve the accuracy of reaching trajectory predictions from MEG signals, addressing a critical bottleneck in real-time BMI performance (21 citations). Her early work on predicting three-dimensional arm trajectories from non-invasive signals laid the groundwork for practical robotic arm control (41 citations). More recently, Kim has explored dynamic frequency and multi-site cortical stimulation to induce artificial somatosensation, a vital step toward closed-loop BMI systems that restore both movement and touch. Her research bridges machine learning, neuroscience, and rehabilitation engineering, offering promising pathways toward intuitive, non-invasive neural prosthetics.