Brain Machine Interfaces based on Subcortical LFP Signals for Neuroprosthetic Control and Neurofeedback Therapy

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Recovering upper limb function will offer a certain degree of independence and sense of autonomy to people with paralysis due to disabling spinal cord injury, amputation, stroke etc. The 'Brain machine interfaces' (BMIs) convert brain signals into control signals for guiding prosthetic arms or other devices, and have showed great potential to restore functions important for everyday life, such as reaching and grasping. However, the translation of the exciting research progress to clinical use that actually improves the daily lives of people with disabilities has barely begun. Key challenges in the clinical applications of existing BMIs include: 1) Difficulties in ensuring stable and satisfactory recordings of brain signals over months or years. Loss of signals over time leads to deterioration in the performance of the neuroprosthetic device and frustration in users. 2) The difficulty in accurately and reliably estimating certain movement parameters such as the gripping force in a simple grasp movement. To date, the best clinical demonstration of BMI has still not been able to accurately manipulate the force level that was applied by a robotic hand.Research into BMI has, to date, almost exclusively focused on signals obtained from the surface or the upper layer of the brain (the cerebral cortex). My previous research has identified the important role of brain signals from the 'basal ganglia', a structure deep inside the brain, in controlling movement and representing gripping force in a grasp. These signals can be readily recorded from electrodes that last many years and such electrodes can be implanted in a relatively safe procedure, which has been a routine therapy for movement disorders. Therefore, these signals offer significant advantages for long-term performance and reliability of the BMI over time. I propose using these deep brain signals to control the grip force of robotic hands, and to study how the patients learn to use the prosthetic hand. This will provide important proof-of-principle of using signal recorded from structure deep inside the brain to control a robotic hand. Meanwhile, understanding and engaging the process of BMI skill learning will potentially be another major opportunity for further improvement of the performance of BMIs.Importantly, what is central to BMI use is for a subject to achieve a specific goal by voluntarily changing their brain activity. But could the BMI be used to train patients to change their own pathological brain activity that is causing problems? Positive answer to this question can lead to novel therapies to diseases where a clear pathological brain signal has been identified. For example, pathological brain activity in the basal ganglia has been heavily associated with motor impairment in a range of diseases, such as Parkinson's disease (PD). I will use the BMI system proposed here to train patients with PD to reduce the pathological signals in the targeted brain area while giving them the feedback about the level of this pathological signal (so called 'neurofeedback training'). I will test the hypothesis that when given feedback, patients are able to reduce the pathological signal that is causing problem in their disease, and that voluntary change of the pathological activity can lead to improvement in movement related symptoms in PD. This work will also help to shed light on the underlying mechanisms of neurofeedback training, which may facilitate other effective clinical applications of this technique.In summary, this work will establish the foundations for novel brain-machine interfaces based on signals recorded from deep brain regions that contain rich information related to movement intention and have been proven to be stable over time. I will use the new framework to control a prosthetics hand with graded gripping force, to provide neurofeedback training to reduce symptoms in PD, and to study the role of basal ganglia in the control and learning of movements.