Publications

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

Manoliu A, Haugg A, Sladky R, Hulka L, Kirschner M, Bruhl AB et al. SmoCuDa: A Validated Smoking Cue Database to Reliably Induce Craving in Tobacco Use Disorder. European Addiction Research. 2021 Mär;27(2):107-114. Epub 2020 Aug 27. doi: 10.1159/000509758

Klink K, Jaun U, Federspiel A, Wunderlin M, Teunissen CE, Kiefer C et al. Targeting hippocampal hyperactivity with real-time fMRI neurofeedback: protocol of a single-blind randomized controlled trial in mild cognitive impairment. BMC Psychiatry. 2021 Feb 9;21(1):87. doi: 10.1186/s12888-021-03091-8

Manoliu A, Sladky R, Scherpiet S, Jäncke L, Kirschner M, Haugg A et al. Dopaminergic neuromodulation has no detectable effect on visual-cue induced haemodynamic response function in the visual cortex: A double-blind, placebo-controlled functional magnetic resonance imaging study. Journal of Psychopharmacology. 2021 Jan;35(1):100-102. Epub 2020. doi: 10.1177/0269881120972341

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

Haugg A, Sladky R, Skouras S, McDonald A, Craddock C, Kirschner M et al. Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity? Human Brain Mapping. 2020 Okt 1;41(14):3839-3854. doi: 10.1002/hbm.25089

Kostorz K, Flanagin VL, Glasauer S. Synchronization between instructor and observer when learning a complex bimanual skill. NeuroImage. 2020 Aug 1;216:116659. doi: 10.1016/j.neuroimage.2020.116659

Scharnowski F, Nicholson A, Eickhoff SB, Koush Y, Pichon S, Rosa MJ et al. The role of the subgenual anterior cingulate cortex in dorsomedial prefrontal - amygdala neural circuitry during positive-social emotion regulation. Human Brain Mapping. 2020 Aug 1;41(11):3100-3118. doi: 10.1002/hbm.25001

Scharnowski F. Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist). Brain: a journal of neurology. 2020 Jun;143(6):1674-1685. doi: 10.1093/brain/awaa009

Scharnowski F. Personode: A Toolbox for ICA Map Classification and Individualized ROI Definition. Neuroinformatics. 2020 Jun;18(3):339-349. doi: 10.1007/s12021-019-09449-4

Scharnowski F. Network-based fMRI-neurofeedback training of sustained attention. NeuroImage. 2020;221:117194. doi: 10.1016/j.neuroimage.2020.117194

Skouras S, Scharnowski F. The effects of psychiatric history and age on self-regulation of the default mode network. NeuroImage. 2019 Sep;198:150-159. 31103786. doi: 10.1016/j.neuroimage.2019.05.008

Kopel R, Sladky R, Laub P, Koush Y, Robineau F, Hutton C et al. No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI. NeuroImage. 2019 Mai 1;191: 421-429. doi: 10.1016/j.neuroimage.2019.02.058

Robineau F, Saj A, Neveu R, Van De Ville D, Scharnowski F, Vuilleumier P. Using real-time fMRI neurofeedback to restore right occipital cortex activity in patients with left visuo-spatial neglect: proof-of-principle and preliminary results. Neuropsychological Rehabilitation. 2019 Apr 6;29(3):339-360. doi: 10.1080/09602011.2017.1301262

Koush Y, Pichon S, Eickhoff SB, Van De Ville D, Vuilleumier P, Scharnowski F. Brain networks for engaging oneself in positive-social emotion regulation. NeuroImage. 2019 Apr 1;189:106-115. doi: 10.1016/j.neuroimage.2018.12.049

Ekanayke J, Ridgeway G, Winston JS, Feredoes E, Razi A, Koush Y et al. Volitional modulation of higher-order visual cortex alters human perception. NeuroImage. 2019 Mär;188:291-301. doi: 10.1016/j.neuroimage.2018.11.054

Sorger B, Scharnowski F, Linden DEJ, Hampson M, Young KD. Control Freaks: Towards Optimal Selection of Control Conditions for Neurofeedback Studies. NeuroImage. 2019 Feb 1;186:256-265. doi: 10.1016/j.neuroimage.2018.11.004

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

Koush Y, Masala N, Scharnowski F, Van De Ville D. Data-driven tensor independent component analysis for model-based connectivity neurofeedback. NeuroImage. 2019 Jan 1;184:214-226. doi: 10.1016/j.neuroimage.2018.08.067

Doerig A, Scharnowski F, Herzog M. Building perception block by block: a response to Fekete et al. Neuroscience of Consciousness. 2019;2019(1):niy012. doi: 10.1093/nc/niy012

Steyrl D. Improving the quality of the electroencephalogram simultaneously recorded with functional magnetic resonance imaging. 2018.

Kirschner M, Sladky R, Haugg A, Stämpfli P, Jehli E, Hodel M et al. Self-regulation of the Dopaminergic Reward Circuit in Cocaine Users with Mental Imagery and Neurofeedback. EBioMedicine. 2018 Nov;37:489-498. doi: 10.1016/j.ebiom.2018.10.052

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

Scharnowski F, Ekanayake J, Hutton C, Ridgway G, Weiskopf N, Rees G. Real-time decoding of covert attention in higher-order visual areas. NeuroImage. 2018 Apr 1;169:462-472. doi: 10.1016/j.neuroimage.2017.12.019

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: 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

Koush Y, Ashburner J, Prilepin E, Sladky R, Zeidman P, Bibikov S et al. Real-time fMRI data for testing OpenNFT functionality. Data in Brief. 2017 Okt;14:344-347. doi: 10.1016/j.dib.2017.07.049

Koush Y, Ashburner J, Prilepin E, Sladky R, Zeidman P, Bibikov S et al. OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis. NeuroImage. 2017 Aug;156:489-503. doi: 10.1016/j.neuroimage.2017.06.039

Scharnowski F, Robineau F, Meskaldji DE, Koush Y, Rieger SW, Mermoud C et al. Maintenance of Voluntary Self-regulation Learned through Real-Time fMRI Neurofeedback. Frontiers in Human Neuroscience. 2017 Mär 23;11:131. doi: 10.3389/fnhum.2017.00131

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

Scharnowski F, Sitaram R, Ros T, Stoeckel L, Haller S, Lewis-Peacock J et al. Closed-loop brain training: the science of neurofeedback. Nature reviews. Neuroscience. 2017 Feb;18(2):86-100. doi: 10.1038/nrn.2016.164

Scharnowski F, Koush Y, Meskaldji DE, Pichon S, Rey G, Rieger SW et al. Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback. Cerebral Cortex. 2017 Feb;27(2):1193-1202. doi: 10.1093/cercor/bhv311

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

Scharnowski F, Kopel R, Emmert K, Haller S, Van De Ville D. Distributed Patterns of Brain Activity Underlying Real-Time fMRI Neurofeedback Training. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2017;64(6):1228-1237. doi: 10.1109/tbme.2016.2598818

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

Scharnowski F, Herzog MH, Kammer T. Time Slices: What Is the Duration of a Percept? PLoS Biology. 2016 Apr 12;14(4):e1002433. doi: 10.1371/journal.pbio.1002433

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

Steyrl D, Schwarz A, Müller-Putz GR. The MIRAGE91 Brain-Computer Interface. 2016. doi: 10.13140/RG.2.2.30530.61125