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We present a novel hierarchical graphical model based context-aware hybridbrain-machine interface (hBMI) using probabilistic fusion ofelectroencephalographic (EEG) and electromyographic (EMG) activities. Based onexperimental data collected during stationary executions and subsequentimageries of five different hand gestures with both limbs, we demonstratefeasibility of the proposed hBMI system through within session and onlineacross sessions classification analyses. Furthermore, we investigate thecontext-aware extent of the model by a simulated probabilistic approach andhighlight potential implications of our work in the field ofneurophysiologically-driven robotic hand prosthetics.

Hierarchical Graphical Models for Context-Aware Hybrid Brain-Machine Interfaces