| High-performance detection of epilepsy in seizure-free EEG recordings: A novel machine learning approach using very specific epileptic EEG sub-bands. R Buettner, J Frick, T Rieg ICIS, 2019 | 64 | 2019 |
| High-performance detection of alcoholism by unfolding the amalgamated EEG spectra using the Random Forests method T Rieg, J Frick, M Hitzler, R Buettner | 50 | 2019 |
| Machine learning based diagnosis of diseases using the unfolded EEG spectra: Towards an intelligent software sensor R Buettner, T Rieg, J Frick Information Systems and Neuroscience: NeuroIS Retreat 2019, 165-172, 2020 | 39 | 2020 |
| Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms T Rieg, J Frick, H Baumgartl, R Buettner PloS one 15 (12), e0243615, 2020 | 33 | 2020 |
| Machine learning-based diagnosis of epilepsy in clinical routine: Lessons learned from a retrospective pilot study T Rieg, J Frick, R Buettner Information Systems and Neuroscience: NeuroIS Retreat 2020, 276-283, 2020 | 7 | 2020 |
| Detection of schizophrenia: A machine learning algorithm for potential early detection and prevention based on event-related potentials. J Frick, T Rieg, R Buettner HICSS, 1-10, 2021 | 6 | 2021 |
| Machine Learning-Based Detection of High Trait Anxiety Using Frontal Asymmetry Characteristics in Resting-State EEG Recordings J Gross, F Mesgun, J Frick, H Baumgartl, R Buettner Machine Learning 7, 12-2021, 2021 | 1 | 2021 |
Thilo RiegCentral Institute of Mental HealthVerified email at zi-mannheim.de
