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

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.

Research into BMI has, to date, almost exclusively focused on signals obtained from the cerebral cortex. The clinical application of BMI faces grand challenges including the lack of longevity in the neural spiking recorded over the cortex for decoding movement related information, and the difficulty in reliable and accurate prediction of gripping force required to develop neuroprosthetics of clinical grade.

My previous research has identified the important role of local field potentials (LFP) recorded from the basal ganglia in encoding movement related information such as motor effort and gripping force, suggesting that these signals can be used to deliver graded force generation via a robotic hand. Compared to neural spikes, the long-term stability of LFP signals offers significant advantages for long-term performance and reliability of the BMI over time. In addition, new developments in implantable devices, which offer the capacity for chronic recording, real-time processing and wireless data transmission, afford an unparalleled opportunity to utilize deep brain signals for BMI control.

I propose to exploit this research opportunity and our understanding about the role of basal ganglia in motor control, to develop new approaches for BMI based on LFP signals recorded from the basal ganglia. I will build the first robotic mechanism interfacing with subcortical structures - the basal ganglia, and use the new framework to control a prosthetics hand with graded gripping force. This will also allow me to investigate the neural basis of the learning process in the use of a neuroprosthetic hand in human subjects. Secondly, I will use the system to provide neurofeedback training to regulate pathological oscillations in the basal ganglia associated with Parksinson's disease, and to study the functional changes in the cortico-basal ganglia network induced by neurofeedback training.

The research in BMI systems generates tremendous excitement from academic community, clinicians and general public. The excitement reflects the rich promise of BMIs. People who may benefit from the proposed research include:

1) Staff working on the project.
Through the project, the PI will further develop her long-term career plan and scientific vision to translate neurosciences and engineering knowledge into clinical therapies in order to recover function and ameliorate symptoms. The Research Associate will gain essential training in biomedical signal processing, real-time implementation of robotic control, experimental design and recording in clinical environment.

2) Healthcare industry.
The current proposal opens the door for new applications of clinical grade implantable pacemakers that also include ultra-low power amplifier and wireless telemetry for data uploads. More clinical use of the device is a new commercial opportunity for the healthcare industry.

3) People suffering from Parkinson's disease.
There are 127,000 people with Parkinson's disease in UK, and 10-20% of them will receive the surgery for deep brain stimulation therapy in the late stage of the disease. They will benefit from the neurofeedback training therapy proposed in the study, especially since DBS pacemakers with the capacity to measure and wirelessly transmit data are becoming widely used in clinical applications. Self-regulation of the pathological neural activities combined with potential plastic changes in the brain will reduce the requirement for medication, or high amplitude of DBS stimulation. This will reduce the side effects that are concomitant with both medication and DBS, and improve the quality of life of the patients.

4) People living with paralysis.
In the UK and Ireland, there estimated to be 50,000 people with paralysis due to spinal cord injury, amputation, stroke and etc. among which the spinal cord injury primarily affects young adults. The cost to the nation is estimated at £1 billion per annum. Recovering upper limb functions will offer both functional independence, and a sense of autonomy, which is of substantial benefit to these people, and will reduce social support required for them. At present, the achievements of BMI research and development remain confined almost entirely to the laboratory. The proposed research will make significant contributions towards making the neural prostheses more reliable in long-term.

5) People suffering from other disease in which neurofeedback therapy may help.
There is increasing number of patients with psychological disorders such as anxiety, depression and post-traumatic stress disorder, who are referred to non-invasive neurofeedback training. Neurofeedback training has also been proposed to improve rehabilitation for people with strokes, head trauma, and other disorders. My study on the potential plastic changes in the larger network of the brain induced by the neurofeedback training will help to shed light on the underlying mechanisms of neurofeedback training that will undoubtedly facilitate more effective clinical applications.

6) Wider public.
There has been widespread and lasting societal interest in the development and research on BMI systems. This is possibly because BMIs not only have the potential to revolutionize the life of many people with motor disabilities, but also revolutionize the way people communicate with the world, interact with information technology and the world itself. Outlined research can benefit the wider public by increasing the awareness of the existence of the technique, and by providing insights into how BMIs can contribute to improving people's life in multiple ways.