Effect of a single session of sensorimotor rhythm neurofeedback training on the putting performance of professional golfers.

Sensorimotor rhythm (SMR) activity has been associated with automaticity and flow in motor execution. Studies have revealed that neurofeedback training (NFT) of the SMR can improve sports performance; however, few studies have adequately explored the effects of a single session of such NFT or examined the possible mechanisms underlying these effects on sports performance. This study recruited 44 professional golfers to address these gaps in the literature. A crossover design was employed to determine the order of the participation in the NFT and no-training control conditions. The participants were asked to perform 60 10-foot putts while electroencephalograms (EEGs) were recorded before and after the tasks. In pre-and post-tests, visual analog scales were used to assess the psychological states associated with SMR activities including the levels of attention engagement, conscious motor control, and physical relaxation. The results revealed that a single NFT session effectively increased SMR power and improved putting performance compared with the control condition. The subjective assessments also revealed that the participants reported lower attention engagement, less conscious control of the motor details and were more relaxed in the putting task, suggesting that SMR NFT promoted effortless and quiescent mental states during motor preparation for a putting task. This study aligns with theoretical hypotheses and extends current knowledge by revealing that a single session of SMR NFT can effectively enhance SMR power and improve putting performance in professional golfers. It also provides preliminary evidence of the possible underlying mechanisms that drive the effect of SMR NFT on putting performances.

A cortically-inspired model for inverse kinematics computation of a humanoid finger with mechanically coupled joints.

The human hand's versatility allows for robust and flexible grasping. To obtain such efficiency, many robotic hands include human biomechanical features such as fingers having their two last joints mechanically coupled. Although such coupling enables human-like grasping, controlling the inverse kinematics of such mechanical systems is challenging. Here we propose a cortical model for fine motor control of a humanoid finger, having its two last joints coupled, that learns the inverse kinematics of the effector. This neural model functionally mimics the population vector coding as well as sensorimotor prediction processes of the brain's motor/premotor and parietal regions, respectively. After learning, this neural architecture could both overtly (actual execution) and covertly (mental execution or motor imagery) perform accurate, robust and flexible finger movements while reproducing the main human finger kinematic states. This work contributes to developing neuro-mimetic controllers for dexterous humanoid robotic/prosthetic upper-extremities, and has the potential to promote human-robot interactions.