These results suggest that learning to control person-specific pre-movement SMR features associated with finger extension can improve finger extension ability after stroke for some individuals. These results merit further investigation in a rehabilitation context.

Brain-machine interfaces (BMI) are powerful devices for restoring function to people living with paralysis. Leveraging significant advances in neurorecording technology, computational power, and understanding of the underlying neural signals, BMI have enabled severely paralyzed patients to control external devices, such as computers and robotic limbs. However, high-performance BMI currently require highly invasive recording techniques, and are thus only available to niche populations. Here, we show that a minimally invasive neuroimaging approach based on functional ultrasound (fUS) imaging can be used to detect and decode movement intention signals usable for BMI. We trained non-human primates to perform memory-guided movements while using epidural fUS imaging to record changes in cerebral blood volume from the posterior parietal cortex -a brain area important for spatial perception, multisensory integration, and movement planning. Using hemodynamic signals acquired during movement planning, we classified left-cued vs. right-cued movements, establishing the feasibility of ultrasonic BMI. These results demonstrate the ability of fUS-based neural interfaces to take advantage of the excellent spatiotemporal resolution, sensitivity, and field of view of ultrasound without breaching the dura or physically penetrating brain tissue.

Classical systems neuroscience positions primary sensory areas as early feed-forward processing stations for refining incoming sensory information. This view may oversimplify their role given extensive bi-directional connectivity with multimodal cortical and subcortical regions. Here we show that single units in human primary somatosensory cortex encode imagined reaches in a cognitive motor task, but not other sensory–motor variables such as movement plans or imagined arm position. A population reference-frame analysis demonstrates coding relative to the cued starting hand location suggesting that imagined reaching movements are encoded relative to imagined limb position. These results imply a potential role for primary somatosensory cortex in cognitive imagery, engagement during motor production in the absence of sensation or expected sensation, and suggest that somatosensory cortex can provide control signals for future neural prosthetic systems.

Brain-computer interfacing is a technology that has the potential to improve patient engagement in robot-assisted rehabilitation therapy. For example, movement intention reduces mu (8-13 Hz) oscillation amplitude over the sensorimotor cortex, a phenomenon referred to as event-related desynchronization (ERD). In an ERD-contingent assistance paradigm, initial BCI-enhanced robotic therapy studies have used ERD to provide robotic assistance for movement. Here we investigated how ERD changed as a function of audio-visual stimuli, overt movement from the participant, and robotic assistance. Twelve unimpaired subjects played a computer game designed for rehabilitation therapy with their fingers using the FINGER robotic exoskeleton. In the game, the participant and robot matched movement timing to audio-visual stimuli in the form of notes approaching a target on the screen set to the consistent beat of popular music. The audio-visual stimulation of the game alone did not cause ERD, before or after training. In contrast, overt movement by the subject caused ERD, whether or not the robot assisted the finger movement. Notably, ERD was also present when the subjects remained passive and the robot moved their fingers to play the game. This ERD occurred in anticipation of the passive finger movement with similar onset timing as for the overt movement conditions. These results demonstrate that ERD can be contingent on expectation of robotic assistance; that is, the brain generates an anticipatory ERD in expectation of a robot-imposed but predictable movement. This is a caveat that should be considered in designing BCIs for enhancing patient effort in roboticallyassisted therapy.