Brain-Computer Interfaces

Restoration of arm and hand control is consistently the most sought after quality of life improvement requested by patients with spinal cord injury. Although brain-computer interfaces for movement have been increasingly successful, BCIs for fast and accurate grasp control have been limited by a lack of methods designed for the complexity of grasping. We are working to develop new algorithms for training BCIs to allow non-human primates and eventually humans to perform reaching and grasping with speed and accuracy. The next step of this work will interface neural signals with skilled artificial systems using a ‘shared control’ approach to allow subjects to act with dexterity in complex simulated and real world environments. These new models will also make real-time predictions about how neural networks coordinate during complex movements, allowing us to causally disambiguate between competing models and make progress in our fundamental understanding of motor control.