Hierarchical Graphical Models for Context-Aware Hybrid Brain-Machine Interfaces
This work addresses the challenge of enhancing robotic hand prosthetics for individuals with limb impairments, though it appears incremental as it builds on existing hybrid BMI methods.
The paper tackled the problem of improving brain-machine interfaces by developing a hierarchical graphical model that fuses EEG and EMG data for context-aware classification of hand gestures, achieving feasibility in within-session and online across-session analyses.
We present a novel hierarchical graphical model based context-aware hybrid brain-machine interface (hBMI) using probabilistic fusion of electroencephalographic (EEG) and electromyographic (EMG) activities. Based on experimental data collected during stationary executions and subsequent imageries of five different hand gestures with both limbs, we demonstrate feasibility of the proposed hBMI system through within session and online across sessions classification analyses. Furthermore, we investigate the context-aware extent of the model by a simulated probabilistic approach and highlight potential implications of our work in the field of neurophysiologically-driven robotic hand prosthetics.