Adaptive Hybrid Brain Computer Interfaces for Assistive Robotic Technologies
Project ID: 2228cd1343 (You will need this ID for your application)
Under Offer
Research Theme: Healthcare Technologies
UCL Lead department: Division of Surgery and Interventional Sciences
Lead Supervisor: Tom Carlson
Project Summary:
WHY THIS RESEARCH IS IMPORTANT
Brain Computer Interfaces (BCIs) allow people with severe motor impairments to turn their thoughts into actions, empowering them to perform activities of daily living independently. However, we need to overcome challenges in decoding accuracy to enable people to use this technology reliably in their daily lives.
WHO YOU WILL BE WORKING WITH
You will be working within Aspire Create (Centre for Rehabilitation Engineering and Assistive Technologies) and the Global Disability Innovation Hub, with Prof Tom Carlson, Professor of Assistive Robotics – an expert in EEG-based motor-imagery BCIs and smart wheelchairs; Dr Hubin Zhao, Lecturer in Medical Technologies – an expert in wearable medical imaging and AI hardware; and Dr Younjun Cho, Associate Professor in Computer Science – an expert in artificial intelligence-powered physiological computing.
WHAT YOU WILL BE DOING
You will be working with electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS). You will further develop our Motor Imagery BCI with machine learning techniques to understand when the user is performing specific motor imagery tasks, and then map these to deliver control commands to our robotic smart wheelchair (both in Virtual Reality simulation and in the physical world). During this project you will develop and evaluate a novel shared control approach to improve the mutual learning process between the human and the computer.
WHO WE ARE LOOKING FOR
We are looking for someone with a strong engineering / computer science / physical sciences background, who is experienced in programming and keen to learn new tools and techniques. This project will involve coding in different languages, from C/C++ to Python and Matlab. You must keep clean, well-documented and version-controlled code.
You must also be enthusiastic about running experiments with human participants – of course we will provide training and support on the relevant methods and equipment.