Neural closed-loop control of a hand prosthesis using cross-modal haptic feedback

Due to the growing field of neuro-prosthetics and other brain-machine interfaces that employ human-like control schemes, it has become a priority to design sensor and actuation mechanisms that relay tactile information to the user. Unfortunately, most state of the art technology uses feedback techniques that are invasive, costly or inefficient for the general population. This paper proposes a feasible feedback method where tactile information during dexterous manipulation is perceived through multi-frequency auditory signals. In the interest of examining whether users are able to quickly learn and adapt to the audio-tactile relationship and apply it to the neural control of a robot, an experimental protocol was formed. Users were instructed to grasp several objects of varying stiffness and weight using an electromyographically-controlled robotic hand, and tactile information was provided to them in real-time through the proposed cross-modal feedback. Results show that users were able to adapt and learn the feedback technology after short use, and could eventually use auditory information alone to control the grasping forces of a robotic hand. This outcome suggests that the proposed feedback method could be a viable alternative for obtaining tactile feedback while staying non-invasive and practical to the user, with applications ranging from neuro-prosthetics to control interfaces for remotely operated devices.