Mail: david.steyrl@univie.ac.at
David’s research focus is on applying machine-learning techniques and statistical signal processing methods to psychological questions, modeling and formalizing exposure therapy, and applying rt-fMRI based neurofeedback to train brain-states with dynamic paradigms.
Past research focused on adaptive (bio-) signal processing and machine learning, with application to simultaneous EEG-fMRI and EEG based Brain-Computer Interfaces.
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2024
Mikuni J, Spee BTM, Forlani G, Leder H, Scharnowski F, Nakamura K et al. Cross-Cultural Comparison of Beauty Judgments in Visual Art Using Machine Learning Analysis of Art Attribute Predictors Among Japanese and German Speakers. Scientific Reports. 2024 Jul 10;14(1):15948. doi: 10.1038/S41598-024-65088-Z
Karner A, Obenaus L, Zhang M, Lor C, Kostorz K, Pegler D et al. Visual attributes of spiders associated with aversiveness in spider-fearful individuals: A machine learning analysis. PsyArXiv. 2024 Jun 14. doi: https://doi.org/10.31234/osf.io/ht2pr
Chiappini E, Massaccesi C, Korb S, Steyrl D, Willeit M, Silani G. Neural Hyperresponsivity During the Anticipation of Tangible Social and Nonsocial Rewards in Autism Spectrum Disorder: A Concurrent Neuroimaging and Facial Electromyography Study. Biological psychiatry. Cognitive neuroscience and neuroimaging. 2024 Apr 18. Epub 2024 Apr 18. doi: 10.1016/j.bpsc.2024.04.006
Siegel M, Steyrl D, Goldberg AE, Nicholson A, Zemp M. Exposure to minority stress and structural stigma predict well-being in LGBTQ+ parents across 19 European countries: An intersectional, machine learning-based approach. 2024 Apr 12. doi: https://doi.org/10.31234/osf.io/hqkxb
Zhang M, Karner A, Kostorz K, Shea S, Steyrl D, Melinšcak F et al. SpiDa-MRI, behavioral and (f)MRI data of adults with fear of spiders. bioRxiv. 2024 Feb 7.
Karner A, Zhang M, Lor CS, Steyrl D, Götzendorfer SJ, Weidt S et al. The "SpiDa" dataset: self-report questionnaires and ratings of spider images from spider-fearful individuals. Frontiers in Psychology. 2024;15:1327367. 1327367. doi: 10.3389/fpsyg.2024.1327367
Spee BTM, Leder H, Mikuni J, Scharnowski F, Pelowski M, Steyrl D. Using Machine Learning to Predict Judgments on Western Visual Art Along Content-Representational and Formal-Perceptual Attributes. PLoS ONE. 2024. doi: 10.1371/journal.pone.0304285
2023
Kostorz K, Nguyen T, Pan Y, Melinšcak F, Steyrl D, Hu Y et al. Towards fNIRS Hyperfeedback: A Feasibility Study on Real-Time Interbrain Synchrony. bioRxiv. 2023 Dez 12. doi: https://doi.org/10.1101/2023.12.11.570765
Park AH, Patel H, Mirabelli J, Eder SJ, Steyrl D, Lueger-Schuster B et al. Machine learning models predict PTSD severity and functional impairment: A personalized medicine approach for uncovering complex associations among heterogeneous symptom profiles. Psychological Trauma. 2023 Nov 27. doi: 10.1037/tra0001602
Lieberman JM, Rabellino D, Densmore M, Frewen PA, Steyrl D, Scharnowski F et al. A tale of two targets: examining the differential effects of posterior cingulate cortex- and amygdala-targeted fMRI-neurofeedback in a PTSD pilot study. Frontiers in Neuroscience. 2023 Nov;17:1229729. doi: 10.3389/fnins.2023.1229729
Spee BTM, Mikuni J, Leder H, Scharnowski F, Pelowski M, Steyrl D. Machine learning revealed symbolism, emotionality, and imaginativeness as primary predictors of creativity evaluations of western art paintings. Scientific Reports. 2023 Aug;13(1):12966. doi: 10.1038/s41598-023-39865-1
Siegel M, Zemp M, Goldberg AE, Nicholson A, Steyrl D. Preregistration: Hidden hearts: Structural stigma and minority stress as predictors of avoiding public display of affection among individuals in same-gender relationships from 28 European countries. 2023. doi: 10.17605/OSF.IO/AR2VH
Siegel M, Steyrl D, Goldberg AE, Nicholson A, Zemp M. Preregistration: Individual-, couple-, and family-level minority stress in LGBT parents from 22 European countries: An intersectional, machine learning-based approach. 2023. doi: 10.17605/OSF.IO/HQTGA
Thanhaeuser M, Steyrl D, Fuiko R, Brandstaetter S, Binder C, Thajer A et al. A secondary outcome analysis of a randomized trial using a mixed lipid emulsion containing fish oil in infants with extremely low birth weight: Cognitive and behavioral outcome at preschool age. The Journal of pediatrics. 2023 Mär;254:68-74.e3. Epub 2022 Okt 15. doi: 10.1016/j.jpeds.2022.10.014
Lieberman JM, Rabellino D, Densmore M, Frewen PA, Steyrl D, Scharnowski F et al. Posterior cingulate cortex targeted real-time fMRI neurofeedback recalibrates functional connectivity with the amygdala, posterior insula, and default-mode network in PTSD. Brain and Behavior. 2023 Mär;13(3):e2883. doi: 10.1002/brb3.2883
Lor CS, Zhang M, Karner A, Steyrl D, Sladky R, Scharnowski F et al. Pre- and post-task resting-state differs in clinical populations. NeuroImage: Clinical. 2023 Jan;37:103345. doi: 10.1016/j.nicl.2023.103345
2022
Giordano V, Luister A, Reuter C, Czedik-Eysenberg I, Singer D, Steyrl D et al. Audio Feature Analysis for Acoustic Pain Detection in Term Newborns. Neonatology. 2022 Dez 1;119(6):760-768. Epub 2022 Sep 16. doi: 10.1159/000526209
Giordano V, Bibl K, Felnhofer A, Kothgassner O, Steinbauer P, Eibensteiner F et al. Relationship between psychological characteristics, personality traits, and training on performance in a neonatal resuscitation scenario: A machine learning based analysis. Frontiers in Pediatrics . 2022 Nov 18;10:1000544. doi: 10.3389/fped.2022.1000544
Lor CS, Zhang M, Karner A, Steyrl D, Sladky R, Scharnowski F et al. Pre- and post-task resting-state differs in clinical populations. bioRxiv. 2022 Sep 22. doi: 10.1101/2022.09.20.508750
Thanhaeuser M, Steyrl D, Fuiko R, Brandstaetter S, Binder C, Thajer A et al. Neurodevelopmental Outcome of Extremely Low Birth Weight Infants with Cholestasis at 12 and 24 Months. Neonatology. 2022 Jul 1;119:501-509. Epub 2022 Jun 9. doi: 10.1159/000525003
Mueller-Putz GR, Coyle D, Lotte F, Jin J, Steyrl D. Editorial: Long Term User Training and Preparation to Succeed in a Closed-Loop BCI Competition. Frontiers in Human Neuroscience. 2022 Mär 24;16:869700. doi: 10.3389/fnhum.2022.869700
Lukacs G, Steyrl D. Machine Learning Mega-Analysis Applied to the Response Time Concealed Information Test: No Evidence for Advantage of Model-Based Predictors Over Baseline. Collabra: Psychology. 2022 Feb 23;8(1):32661. doi: 10.1525/collabra.32661
2021
Krylova M, Skouras S, Razi A, Nicholson AA, Karner A, Steyrl D et al. Progressive modulation of resting-state brain activity during neurofeedback of positive-social emotion regulation networks. Scientific Reports. 2021 Dez 3;11(1):23363. doi: 10.1038/s41598-021-02079-4
Eder SJ, Stefanczyk MM, Pieniak M, Martinez-Molina J, Binter J, Pesout O et al. Dangers and Strangers: Pathogenic threat, fear, and perceived vulnerability do not predict ethnocentric orientations during the COVID-19 pandemic in Europe. Human Ethology. 2021 Nov 5;36:125-137. doi: 10.22330/he/36/125-137
Aue T, Hoeppli M-E, Scharnowski F, Steyrl D. Contributions of diagnostic, cognitive, and somatovisceral information to the prediction of fear ratings in spider phobic and non-spider-fearful individuals. Journal of Affective Disorders. 2021 Nov 1;294:296-304. doi: 10.1016/j.jad.2021.07.040
Haugg A, Renz FM, Nicholson AA, Lor C, Götzendorfer SJ, Sladky R et al. Predictors of Real-Time fMRI Neurofeedback Performance and Improvement - a Machine Learning Mega-Analysis. NeuroImage. 2021 Aug 15;237:118207. doi: 10.1016/j.neuroimage.2021.118207
Eder SJ, Nicholson AA, Stefanczyk MM, Pieniak M, Martinez-Molina J, Pesout O et al. Securing Your Relationship: Quality of Intimate Relationships During the COVID-19 Pandemic Can Be Predicted by Attachment Style. Frontiers in Psychology. 2021 Jul 21;12:647956. doi: 10.3389/fpsyg.2021.647956
Eder SJ, Steyrl D, Stefanczyk MM, Pieniak M, Molina JM, Pešout O et al. Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study. PLoS ONE. 2021 Mär;16(3):e0247997. doi: 10.1371/journal.pone.0247997
2020
Eder SJ, Stefańczyk MM, Pieniak M, Molina JM, Binter J, Pešout O et al. Food insecurity, hoarding behavior, and environmental harshness do not predict weight changes during the COVID-19 pandemic. Human Ethology Bulletin. 2020 Dez 8;35:122-136. doi: 10.22330/he/35/122-136
Morawetz C, Steyrl D, Berboth S, Heekeren HR, Bode S. Emotion Regulation Modulates Dietary Decision-Making via Activity in the Prefrontal–Striatal Valuation System. Cerebral Cortex. 2020 Nov;30(11):5731–5749. doi: 10.1093/cercor/bhaa147
2019
Steyrl D, Müller-Putz GR. Artifacts in EEG of simultaneous EEG-fMRI: pulse artifact remainders in the gradient artifact template are a source of artifact residuals after average artifact subtraction. Journal of Neural Engineering. 2019 Feb;16(1):016011. doi: 10.1088/1741-2552/aaec42
2018
Steyrl D. Improving the quality of the electroencephalogram simultaneously recorded with functional magnetic resonance imaging. 2018.
Schulz L, Ischebeck A, Wriessnegger SC, Steyrl D, Müller-Putz GR. Action affordances and visuo-spatial complexity in motor imagery: An fMRI study. Brain and Cognition. 2018 Jul;124:37-46. doi: 10.1016/j.bandc.2018.03.012
Steyrl D, Krausz G, Koschutnig K, Edlinger G, Müller-Putz GR. Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF). Brain Topography: journal of functional neurophysiology. 2018 Jan;31(1):129–149. Epub 2017 Nov 9. doi: https://doi.org/10.1007/s10548-017-0606-7
2017
Statthaler K, Schwarz A, Steyrl D, Kobler R, Höller MK, Brandstetter J et al. Cybathlon experiences of the Graz BCI racing team Mirage91 in the brain-computer interface discipline. Journal of NeuroEngineering and Rehabilitation . 2017 Dez;14(1):129. doi: 10.1186/s12984-017-0344-9
Steyrl D, Krausz G, Koschutnig K, Edlinger G, Müller-Putz GR. Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI. Journal of Neural Engineering. 2017 Feb 3;14(2):026003. doi: 10.1088/1741-2552/14/2/026003
Schwarz A, Steyrl D, Müller-Putz G. Brain-Computer Interface adaptation for an end user to compete in the Cybathlon. in 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. 2017. S. 1803-1808 doi: 10.1109/SMC.2016.7844499
Müller-Putz G, Steyrl D, (ed.), Wriessnegger SC, (ed.), Scherer R, (ed.). Proceedings of the 7th Graz Brain-Computer Interface Conference 2017: From Vision to Reality. Graz: Verlag der Technischen Universität Graz, 2017. doi: 10.3217/978-3-85125-533-1
2016
Wriessnegger SC, Steyrl D, Koschutnig K, Müller-Putz GR. Cooperation in mind: Motor imagery of joint and single actions is represented in different brain areas. Brain and Cognition. 2016 Nov;109:19-25. doi: 10.1016/j.bandc.2016.08.008
Schwarz A, Steyrl D, Höller MK, Statthaler K, Müller-Putz G. BCI adaption for end user: The GRAZ-BCI approach. in Cybathlon Symposium Booklet, ETH. 2016
Höller MK, Schwarz A, Steyrl D, Statthaler K, Müller-Putz G. First contact screening of a BCI Pilot. in Cybathlon Symposium Booklet, ETH. 2016 doi: 10.13140/RG.2.2.23819.72482
Statthaler K, Steyrl D, Schwarz A, Höller MK, Müller-Putz G. Optimized individual mental tasks to control BCIs. in Cybathlon Symposium Booklet, ETH. Kloten: ETH Zurich. 2016. S. 23-23
Steyrl D, Schwarz A, Müller-Putz G. The MIRAGE91 Brain–Computer Interface. in Cybathlon Symposium Booklet, ETH. Kloten: ETH Zurich. 2016. S. 77-77
Steyrl D, Kobler R, Müller-Putz G. On similarities and differences of invasive and non-invasive electrical brain signals in brain-computer interfacing. Journal of Biomedical Science and Engineering. 2016 Jun 30;9(8):393-398. doi: 10.4236/jbise.2016.98034
Müller-Putz G, Schwarz A, Steyrl D. Mirage91: The Graz BCI-Racing Team – making students familiar with BCI research. in Proceedings of the 6th International Brain-Computer Interface Meeting, Asilomar Conference Center, Pacific Grove, California, USA. 2016 doi: 10.3217/978-3-85125-467-9-63
Steyrl D, Scherer R, Faller J, Müller-Putz GR. Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier. Biomedical Engineering / Biomedizinische Technik. 2016 Feb 1;61(1):77-86. doi: 10.1515/bmt-2014-0117
Schwarz A, Steyrl D, Höller MK, Statthaler K, Müller-Putz GR. BCI adaptation for end users The Graz-BCI approach. 2016. doi: 10.13140/RG.2.2.17108.83846
Steyrl D. Improving the vividness of motor imagery tasks for future application in Brain-Computer Interfaces. 2016. doi: 10.13140/RG.2.2.13753.39528
Schwarz A, Scherer R, Steyrl D, Faller J. Mirage91: The Graz BCI-Racing Team - making students familiar with BCI research. 2016. doi: 10.13140/RG.2.2.25497.44641
Müller-Putz G, (ed.), Huggins J, (ed.), Steyrl D, (ed.). Proceedings of the 6th International Brain-Computer Interface Meeting: BCI Past, Present, and Future. Graz: Verlag der Technischen Universität Graz, 2016. 261 S. doi: 10.3217/978-3-85125-467-9
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