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Updated: Feb 15, 2026
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Eye-Tracking Control to Assess Cognitive Functions in Patients with Amyotrophic Lateral Sclerosis
Published on: October 13, 2016
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Machine Learning EEG to Predict Cognitive Functioning and Processing Speed Over a 2-Year Period in Multiple Sclerosis
Hanni Kiiski1, Lee Jollans1, Seán Ó Donnchadha2
1School of Psychology and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
Brain Topography
|January 31, 2018
Summary
Visual event-related potentials (ERPs) can predict cognitive function and processing speed in Multiple Sclerosis (MS) patients and controls. Auditory ERPs did not show predictive value in this longitudinal study.
Area of Science:
- Neuroscience
- Cognitive Science
- Biomedical Engineering
Background:
- Cognitive impairment is a common symptom in Multiple Sclerosis (MS).
- Objective biomarkers are needed to track cognitive changes in MS.
- Event-related potentials (ERPs) offer a non-invasive measure of neural activity.
Purpose of the Study:
- To investigate if ERPs from oddball tasks can predict individual cognitive functioning and processing speed in MS patients and controls.
- To assess the predictive utility of visual and auditory ERPs over a 26-month period.
Main Methods:
- Seventy-eight participants (35 MS patients, 43 controls) underwent EEG recordings during visual and auditory oddball tasks.
- High-density EEG data (128-channel) were collected at baseline, 13, and 26 months.
- Machine learning (penalized linear regression) analyzed spatio-temporal ERP data to predict cognitive composite scores.
Main Results:
- Visual ERPs successfully predicted cognitive functioning and processing speed at baseline and follow-up.
- Auditory ERPs did not demonstrate predictive capability for cognitive performance.
- The findings highlight the potential of visual ERPs as objective neurophysiological markers.
Conclusions:
- Visual ERPs, analyzed with machine learning, show promise for predicting cognitive outcomes in MS and healthy individuals.
- This approach could aid in monitoring disease progression and treatment efficacy.
- Further research is warranted to explore the clinical application of ERP-based prediction models.
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