Mobile-EEG based identification and tracking of Multiple Sclerosis
Research type
Research Study
Full title
Feasibility of Mobile-EEG and machine learning methods for the identification and tracking of Multiple Sclerosis
IRAS ID
163674
Contact name
Barry Devereux
Contact email
Sponsor organisation
Queen's University Belfast, Research Governance, Ethics and Integrity
Duration of Study in the UK
0 years, 11 months, 29 days
Research summary
Multiple Sclerosis is a debilitating condition that can appear without warning, and that develops in unpredictable ways for individuals that have the disease. It may be diagnosed quite late using expensive and invasive methods (fMRI, lumbar puncture), and sufferers lack an easy way to monitor and predict the progress of their condition.
EEG (electroencepholography, or "brainwave" technology) has long been used as a diagnostic tool in clinics and research labs. Now wearable EEG headsets are available as consumer products, and can be used in the home. Computational methods (including signal processing and machine learning) can be used to extract diagnostic biomarkers from these recordings.
In this pilot study we will investigate the feasibility of using mobile EEG technology to:
a) tell if a person may have Multiple Sclerosis or not
b) discriminate whether a person is in an early or late stage of the diseaseREC name
HSC REC B
REC reference
15/NI/0234
Date of REC Opinion
16 Nov 2015
REC opinion
Further Information Favourable Opinion