Marius Tröndle

This study shows non-neural sources like eye and muscle artifacts affect aperiodic EEG parameters, leading to biased interpretations, and proposes a method to correct these biases.

This paper develops a deep learning model to automate scoring of the Rey-Osterrieth Complex Figure (ROCF) drawings, outperforming human raters and clinicians in accuracy and consistency.

The paper shows that reduced frontal alpha asymmetry during sad mood induction predicts antidepressant relapse after medication discontinuation.

The paper investigates age-related changes in EEG alpha power by decomposing signals into aperiodic and adjusted components across three samples of young and elderly individuals.

This study decomposes alpha oscillation power into periodic and aperiodic components, revealing distinct developmental trajectories and linking periodic changes to thalamocortical connectivity and attentional performance.

This paper develops multinational qEEG norms using Hermitian Riemannian methods, showing improved diagnostic accuracy for brain dysfunction and providing harmonization tools.

The paper introduces a machine learning benchmark for classifying reading tasks using EEG and eye-tracking data, providing a public dataset, baselines, and an evaluation platform.