Ira Haraldsen

Uncertainty in Deep Learning for EEG under Dataset Shifts

This study evaluates how ensemble learning strategies affect performance and uncertainty estimation in EEG classification under dataset shifts.

Assessing the robustness of deep learning based brain age prediction models across multiple EEG datasets

This study evaluates deep learning models for predicting brain age from EEG data across multiple datasets, finding that performance varies with dataset shifts and that certain hyperparameters improve generalization.

Learning Residual-based Biomarkers of Cognitive Health via Self-Supervised Learning on EEG State Transitions

This paper introduces a self-supervised learning framework for EEG-based biomarker discovery of cognitive health, but finds poor predictive performance using plasma p-tau217 as a proxy.

Neurologically altered brain activity may not look like aged brain activity: Implications for brain-age modeling and biomarker strategies

This study shows that brain-age gaps from EEG spectral power in neurological populations do not reflect accelerated aging, challenging the use of EEG as a biomarker for neurodegeneration.

EEG Electrodes and Where to Find Them: Automated Localization From 3D Scans

This paper presents a method for accurately localizing EEG electrode positions using 3D scans, validated on a large dataset, achieving 85.7% to 91.0% accuracy without requiring MRI.