David Steyrl

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;14:15948.

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

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

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

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

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

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

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

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

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