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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, wedoi:10.1109/embc.2018.8512677 pmid:30440783 pmcid:PMC6525618 fatcat:z3y2xpq3rfelpbc2owoux7pxty