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Combining wearable EEG with AI to enable at-home, proactive brain health monitoring
The Case for Wearable Brain Health Monitoring
The Case for Wearable Brain Health Monitoring
Dementia affects over 57 million people globally. Early intervention has the potential to delay or prevent disease progression, and neurological biomarkers offer a promising avenue for this. However, the clinical tools currently used for early detection - such as MRI and high-density EEG - are costly infeasible for regular or at-home monitoring.
Ear-EEG has been proposed as an accessible, cost-effective alternative for obtaining EEG outside of a clinical setting. This research investigates whether low-density, ear-based EEG can support brain-health monitoring outside of a clinical setting and enable early detection of cognitive decline.
Anneliese Walsh - PhD Candidate
Anneliese Walsh - PhD Candidate
Anneliese Walsh graduated with distinction from Trinity College Dublin with a B.A.I. and M.A.I. in Electronic and Computer Engineering. Before beginning her PhD, she gained industry experience abroad in Switzerland and Sweden.
Anneliese returned to academia and received the Trinity Research Doctorate Award (TRDA) to fund her PhD work, which focuses on ear-level estimation of brain age from EEG.
By developing a trackable metric for brain-health based on wearable EEG, she hopes to build towards proactive instead of reactive brain-health monitoring.
Research Aims
Research Aims
1. Improving machine learning models for low density EEG
1. Improving machine learning models for low density EEG
2. Investigating brain health biomarkers for real world monitoring
2. Investigating brain health biomarkers for real world monitoring
Publications
Publications
Multiclass Differentiation of Dementia Subtypes Based on Low-Density EEG Biomarkers: Towards Wearable Brain Health Monitoring
Multiclass Differentiation of Dementia Subtypes Based on Low-Density EEG Biomarkers: Towards Wearable Brain Health Monitoring
Walsh A, Shanker S, Lopez Valdes A. Multiclass Differentiation of Dementia Subtypes Based on Low-Density EEG Biomarkers: Towards Wearable Brain Health Monitoring. Journal of Dementia and Alzheimer's Disease. 2025; 2(4):48. https://doi.org/10.3390/jdad2040048
Classifying Neurodegenerative Diseases from Selected Temporal Electrodes: Towards Ear-EEG Applications
Classifying Neurodegenerative Diseases from Selected Temporal Electrodes: Towards Ear-EEG Applications
A. Walsh, S. Shanker and A. L. Valdes, "Classifying Neurodegenerative Diseases from Selected Temporal EEG Electrodes: Towards Ear-EEG Applications," 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Copenhagen, Denmark, 2025, pp. 1-4, doi: 10.1109/EMBC58623.2025.11254741
Research Highlights
Research Highlights
Conferences
Conferences
Alzheimer Europe 2026 - poster presentation
IEEE EMBC 2025 - oral presentation
Awards
Awards
Trinity Research Doctorate Award (Trinity College Dublin, 2024-2028)
Best presentation award (DRNI Early Career Research Day, 2025)
Best presentation award (TCD School of Engineering Postgraduate Research Symposium, 2024)
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