Christoffer Hatlestad‐Hall

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.

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.

Evidence for reduced long-term potentiation-like visual cortical plasticity in schizophrenia and bipolar disorder

The paper assesses LTP-like visual cortical plasticity in schizophrenia and bipolar disorder using VEP modulation, finding impaired N1b modulation in SZ and BDI but not BDII, linked to symptom severity.

Experience-dependent modulation of the visual evoked potential: testing effect sizes, retention over time, and associations with age in 415 healthy individuals

The paper examines VEP modulation in 415 healthy individuals, finding significant effects for several components and age-related correlations, supporting VEP as a measure of synaptic plasticity.