Real-time fMRI data for testing OpenNFT functionality

Publication date: October 2017
Source:Data in Brief, Volume 14
Author(s): Yury Koush, John Ashburner, Evgeny Prilepin, Ronald Sladky, Peter Zeidman, Sergei Bibikov, Frank Scharnowski, Artem Nikonorov, Dimitri Van De Ville
Here, we briefly describe the real-time fMRI data that is provided for testing the functionality of the open-source Python/Matlab framework for neurofeedback, termed Open NeuroFeedback Training (OpenNFT, Koush et al. [1]). The data set contains real-time fMRI runs from three anonymized participants (i.e., one neurofeedback run per participant), their structural scans and pre-selected ROIs/masks/weights. The data allows for simulating the neurofeedback experiment without an MR scanner, exploring the software functionality, and measuring data processing times on the local hardware. In accordance with the descriptions in our main article, we provide data of (1) periodically displayed (intermittent) activation-based feedback; (2) intermittent effective connectivity feedback, based on dynamic causal modeling (DCM) estimations; and (3) continuous classification-based feedback based on support-vector-machine (SVM) estimations. The data is available on our public GitHub repository:http://ift.tt/2whuUcK.



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