  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.
