Real-time fMRI data for testing OpenNFT functionality
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Date
2017-10
Publication Type
Journal Article
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yes
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Abstract
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:
https://github.com/OpenNFT/OpenNFT_Demo/releases.
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Publication status
published
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Book title
Journal / series
Volume
14
Pages / Article No.
344 - 347
Publisher
Elsevier
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Edition / version
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Software
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Date collected
Date created
Subject
OpenNFT; Neurofeedback; Real-time fMRI; Activity; Connectivity; Multivariate pattern analysis