Author

Payam S. Shabestari

University of ZurichORCID 0000-0002-2647-4891Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:electronic health records· 3 Research Focus:benchmarking· 2 Research Focus:cell type annotation· 2 Research Focus:clustering· 1 Research Focus:differential expression· 1 Approach:graph neural network· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Adrian NaasiD· 3 shared Patrick K. A. NeffiD· 3 shared Tobias KleinjungiD· 3 shared Andreas SondereggeriD· 1 shared Berthold LangguthiD· 1 shared Christopher Robin CederrothiD· 1 shared Danpeng Cai· 1 shared Delphine RibesiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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3 shared papersNeuroscience

Adrian NaasORCID

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3 papers

Recent·Most cited

The hedonic evaluation of neurofeedback stimuli is fast, automatic and implicit: An ERP study on stimulus design.

Adrian Naas, Danpeng Cai, Payam S. Shabestari, Tobias Kleinjung, Delphine Ribes, Patrick K. A. Neff, Andreas Sonderegger

This study examines how aesthetic design principles affect neurofeedback (NFB) stimulus evaluation using ERP responses, finding rapid and implicit hedonic reactions to stimuli.

bioRxiv · Jun 10, 2026 · PreprintNeuroscience

Frequency-Specific Resting-State MEG Network Characteristics of Tinnitus Patients Revealed by Graph Learning

Payam S. Shabestari, Hamid Behjat, Dimitri Van De Ville, Christopher Robin Cederroth, Niklas K. Edvall, Adrian Naas, Tobias Kleinjung, Patrick K. A. Neff

This study uses graph learning to analyze resting-state MEG data and identifies frequency-specific functional connectome differences in tinnitus patients compared to controls.

bioRxiv · Mar 10, 2025 · PreprintNeuroscience

Prediction of acoustic tinnitus suppression using resting state EEG: An explainable AI approach

Payam S. Shabestari, Stefan Schoisswohl, Zino Wellauer, Adrian Naas, Tobias Kleinjung, Martin Schecklmann, Berthold Langguth, Patrick K. A. Neff

This paper uses explainable AI to predict acoustic tinnitus suppression from resting state EEG data, identifying key neural features associated with the ability to suppress tinnitus.

bioRxiv · Apr 16, 2024 · PreprintNeuroscience