| layout | title | order |
|---|---|---|
page |
References |
99 |
For software support contact us at [email protected].
- For open-source OpenNFT code and applied real-time data processing and software features, cite Koush et al., 2017, Neuroimage.
- For real-time fMRI data, cite Koush et al., 2017, Data in Brief.
- For SPM features, cite SPM12 http://fil.ion.ucl.ac.uk/spm/.
- For DCM features, cite used SPM, DCM10 release.
- For incremental GLM, cite Bagarinao et. al. 2003, NeuroImage .
- For connectivity-based feedback using DCM, cite Koush et al. 2013, NeuroImage; Koush et al. 2015, Cerebral Cortex.
- For real-time signal processing using Kalman filter, cite Koush et al. 2012, NeuroImage.
- For SVM-based feedback, cite LaConte et al. 2007, HBM; LaConte 2011, NeuroImage
- For sigmoidal function, cite deBettencourt et al. 2015, NatNeuroscience
- For feedback display based on PTB-3 functions, cite http://psychtoolbox.org/
- For Matlab, cite MathWorks, Natick, Massachusetts, United States.
- For Python, cite https://www.python.org/
- Koush Y., Ashburner J., Prilepin E., Sladky R., Zeidman P., Bibikov S., Frank Scharnowski F., Nikonorov A., Van De Ville D. (2017). OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis. Neuroimage 157:489-503)
- Koush Y., Ashburner J., Prilepin E., Sladky R., Zeidman P., Bibikov S., Frank Scharnowski F., Nikonorov A., Van De Ville D. (2017). Real-time fMRI data for testing OpenNFT functionality. Data in Brief 14:344-347