Hugo L. Hammer

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

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.

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.

This paper introduces an AI system that forecasts future embryo morphology in time-lapse videos using Convolutional LSTM layers, showing accurate predictions for cleavage and blastocyst stages.

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.