Hybrid Brain-Computer Interface for Communicating with Patients with Locked-In-Syndrome Caused by Severe Motor Disability

Overview

Communication is a basic human activity necessary for normal life. The United Nations Convention on the Rights of Persons with Disabilities asserts that assisted communication be made available where needed. There are an estimated 350,000 people in the UK who can benefit from assisted communication, with several hundred of these in a severe medical condition known as the locked-in-syndrome. In this project we are measuring brain activity from volunteers non-invasively and analysing the data with the immediate goal of decoding their mental states and responses to auditory stimuli. The results will form the basis for a practical system through which patients can communicate with others.

Addressing the Challenge

Recent progress in technology has allowed Brain-Computer-Interfaces (BCI) to move beyond traditional electroencephalography (EEG) toward multi-modal approaches. Functional near-infrared spectroscopy (fNIRS) is a non-invasive technique that measures local hemodynamic activity through optical sources and detectors placed on the scalp. Its convenience and portability are comparable to EEG. In this project we deploy wearable, concurrent EEG and fNIRS. These modalities provide information that is complementary, compensate for each other's artifacts, and can inform about neurovascular coupling which neither technique can measure by itself. Our goal is to to develop and optimise a methodology for decoding brain states and develop means of communicating with patients with motor neurone disease (MND).

People

This project is being carried out by Dr Ahmet Omurtag and Prof Amin Al-Habaibeh. Dr. Omurtag (PI) has long-standing experience in the applications of EEG and fNIRS and Prof Al-Habaibeh (Co-I) is an internationally recognised expert in artificial intelligence and medical device development.

Making a difference

The need for assisted communication may arise from conditions that include widespread public health problems such as stroke, autism, head injury, and MND. BCI based communication aims to tap into brain activity patterns that form the natural basis of a person's thoughts, to help them maintain contact with their family and care-givers. Few studies seriously address the problem of designing BCI systems to meet the long-term needs of clinical end-users. We plan to use the advantages of EEG+fNIRS to overcome the existing translational gap.