Kab-Mun Cha

About

Kab-Mun Cha is a pioneering researcher in brain-computer interfaces (BCIs), specializing in decoding human movement intentions from electroencephalography (EEG) signals. His work centers on hybrid paradigms that combine Motor Imagery (MI) and Steady-State Somatosensory Evoked Potentials (SSSEP), with a focus on practical applications for exoskeleton control and rehabilitation. Cha’s most-cited study (2019, 8 citations) demonstrates a novel hybrid MI-SSSEP approach that significantly improves classification accuracy for left and right movement intentions, offering a more robust framework for real-time BCI systems. In a complementary study (2019, 4 citations), he developed algorithms to decode sit-to-stand movement intentions using vibrotactile SSSEP, advancing assistive technologies for mobility-impaired individuals. By integrating sensory stimulation with cognitive motor tasks, Cha’s research bridges the gap between neural signal processing and wearable robotics, achieving notable precision in decoding complex movements. His contributions hold promise for enhancing the autonomy of users through intuitive, non-invasive BCI-controlled exoskeletons, marking a key step toward seamless human-machine interaction in clinical and daily-life settings.

Research Focus

Key Achievements

Top Papers

1. 1
   Hybrid MI-SSSEP Paradigm for classifying left and right movement toward BCI for exoskeleton control

   8 citations · 2019
2. 2
   Steady-State Somatosensory Evoked Potential based Brain-Computer Interface for Sit-to-Stand Movement Intention

   4 citations · 2019

Key Collaborators

Contact & Links