Using brain signals to control symptoms and assistive devices
Research type
Research Study
Full title
Brain Machine Interfaces based on subcortical LFP Signals for Neuroprosthetic Control and Neurofeedback Therapy
IRAS ID
220480
Contact name
Huiling Tan
Contact email
Sponsor organisation
Oxford Radcliffe Hospital NHS Trust
Duration of Study in the UK
2 years, 11 months, 28 days
Research summary
Brain machine interfaces (BMIs) convert neural signals from the brain into control signals to guide prosthetic arms or other devices, and have showed great potential to restore functions important for everyday life, such as reaching and grasping. However, research into BMI has, to date, almost exclusively focused on neuronal signals obtained from the surface or the top layer of the brain.
This study aims to test whether the signals recorded from the electrodes implanted in the brain, which are used as a standard clinical treatment for Parkinson's disease and Essential Tremor, can be used to drive a robotic hand or control what happens on a computer screen. We will also test whether signals recorded from deep inside the brain have advantages over those recorded from the surface of the head in this context. If this proves to be the case then these brain signals could be used in the future to help paralysed patients control and interact with their surroundings. In addition, this study may potentially allow patients with Parkinson’s disease to change their own brain signals and thereby improve those symptoms associated with particular patterns of brain waves.
REC name
South Central - Oxford C Research Ethics Committee
REC reference
18/SC/0006
Date of REC Opinion
31 Jan 2018
REC opinion
Favourable Opinion