Towards machine to brain interfaces: Sensory stimulation enhances sensorimotor dynamic functional connectivity in upper limb amputees

Objective. Recent development of sensory stimulation techniques demonstrates the ability to elicit touch-like phantom sensations in upper limb amputees. The cortical processing of this phantom sensation and the corresponding influences on sensorimotor functional connectivity have not been studied. We hypothesize that sensory stimulation has a profound impact on the sensorimotor cortical functional interactions, which will be uncovered by dynamic functional connectivity (dFC) analysis of amputees' electroencephalogram (EEG) recordings. Approach. We investigated dFC between cortical areas associated with somatosensory, motor, visual, and multisensory processing functions using EEG signals. We applied dFC to the EEG of two amputees performing hand movements with and without sensory stimulation and compared the results with those from three able-bodied subjects. We quantified the changes due to sensory stimulation using dFC metrics, namely temporal distance, number of connection paths, temporal global and local efficiencies, and clustering coefficient. Main results. We show a significant effect of sensory stimulation on functional connectivity in the amputee brains, with notable facilitation on multisensory processing among the cortical systems involved in sensorimotor processing. The dFC metrics reveal that sensory stimulation enhances the speed of information transfer (shown by decreases in temporal distance) and the number of connection paths between the brain systems involved in sensorimotor processing, including primary somatosensory and motor, and higher order processing regions. Significance. This is the first work to reveal dynamic communication between somatosensory, motor, and higher order processing regions in the cortex of amputees in response to sensory stimulation. We believe that our work provides crucial insights into the cortical impact of sensory stimulation in amputees, which may lead to the design of personalized brain-informed sensory feedback paradigms. This in turn may lead to building novel Machine to Brain Interfaces involving sensory feedback and the resultant enhanced motor performance.