Brain machine interfaces (BMIs) aim to help patients with paralysis to use their recorded brain activity to control assistive devices.  BMI research requires the collaboration of neuroscientists, engineers, computational neuroscientists, and clinicians.  Generally motor cortex has been used as a source of control signals.  With our U01 BRAIN Initiative grant and other NIH grant support, we have examined a different area of cortex for BMI control, the posterior parietal cortex (PPC).  The PPC forms the intentions and plans to make movements.  Richard Andersen will discuss the finding that PPC represents many intended movement variables over essentially the entire body.  Cognitive variables include goals, cognitive strategies, action observation, and action semantics.  So many variables can be decoded from just a few hundred PPC neurons because they are represented in a clever, partially mixed structure.  Spencer Kellis will describe how intracortical microstimulation of primary somatosensory cortex produces cutaneous and proprioceptive sensations in the limb of a tetraplegic subject.  Being able to provide this naturalistic feedback is important for dexterous robotic control.  Tyson Aflalo will discuss a study in which microelectrode arrays are implanted in both PPC and primary motor cortex.  This study establishes different motor functions for these two areas that can complement brain control and maintain their basic functional differences even after many months of BMI sessions.  Charles Liu will describe the clinical aspects that guide every step of human BMI studies from recruitment, to surgery, to long term care of the participants.  He will highlight the importance of surgical techniques and the support of rehabilitation hospitals.

Learning Objectives:

1. Explain how mixed selectivity allows the posterior parietal cortex to represent so many action variables with a small number of neurons

2. Identify why restoring somatosensory feedback is important for brain-machine interfaces that control robotic hands.

3. Account for why the lack of functional restructuring of the posterior parietal cortex and motor cortex with brain-machine interface usage makes it important to select the right cortical areas for recording brain signals.

4. Enumerate ways that human brain-machine interface research benefits from having multidisciplinary research teams.