Robotic hands can be of valuable assistance to individuals with upper-limb motor disabilities such as stroke survivors, tetraplegic individuals and amputees. Brain-computer interfaces (BCI) offer an intuitive means to control such assistive robotic manipulators. Further, they can potentially promote rehabilitation and recovery in patients with brain or spinal cord lesions. For instance, in neurofeedback (NF) studies, participants visualise a graphical (i.e., thermometer) representation of their own brain activity and attempt to increase this activation, which can result in motor improvements (Mottaz et al., 2018; Sanders et al., 2022). An alternative is to use these neural signals to control a robotic hand to perform realistic movements (Batzianoulis et al; 2021). This approach has three main aims: to activate the target motor network and promote recovery via neuroplasticity, to provide realistic feedback instead of abstract, and to control the robotic arm to perform tasks using the brain’s motor representation network.
EEG has been the preferred method for NF and BCI, due to its portability, cheaper set up and high temporal resolution. However, fMRI superior spatial resolution allows for measuring individual finger representation, which will be an advantage in these earlier stages of development of brain robot interaction. In this PhD project we aim to answer the following question: can we use the brain’s fingers representation system to effortlessly control the robot’s individual fingers? Therefore, the goal of this PhD project is to develop analysis tools to control the Shadow Robotic hand’s individual fingers using fMRI data.
The student will collect fMRI data from healthy participants while they perform real finger movements inside a 7T scanner. The fMRI data will be analyzed offline, and reinforcement learning models will be trained to obtain the robot path planning strategy for controlling the corresponding robot fingers in real-time using the traditional PID control method. In the second stage of the project, participants will control the robot fingers in real-time inside the MRI scanner using their own finger movements. Finally, naive participants will be trained to control the robot fingers without performing real movements. Future work aims to test this protocol in patients, such as stroke survivors, to promote motor recovery or in immobilized patients to use the robot as an assistive device. The student must have strong coding skills, such as C++ and Python, and experience in signal processing, such as noise removal and filtering techniques. They will program and control the Shadow robot fingers virtually in the Isaac Gym simulator and in the RoS system to control the Shadow robot hand in real-time.
The project’s impact will be broad, affecting health, neuroscience, and the robotic society. The output of the project can be published in various journals and conferences, such as IEEE International Conference on Robotics and Automation (ICRA), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Science Robotics, Journal of Neural Engineering, and RTFIN conference.
Eligibility
Applicants must have or expect to obtain the equivalent of a 1st or 2:1 degree in any subject relevant to the CDT including, but not limited to, computing science, psychology, linguistics, mathematics, sociology, engineering, physics, etc.
Applicants will be asked to provide two references as part of their application.
Funding
Funding is available to cover the annual tuition fees for UK home applicants, as well as an annual stipend at the standard UKRI rate (currently £17,668 for 2022/23). To be classed as a home applicant, candidates must meet the following criteria:
- Be a UK National (meeting residency requirements), or
- Have settled status, or
- Have pre-settled status (meeting residency requirements), or
- Have indefinite leave to remain or enter.
As per UKRI funding guidelines, up to 30% of studentships may be awarded to international applicants who do not meet the UK home status requirements. Funding for successful international students will match that of home students and no international top-up fees will be payable.
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