Cox, R. W. (1996). AFNI: Programvare for analyse og visualisering av funksjonell magnetisk resonans Neuroimages. Comput Biomed Res, 29, 162–73.
Article CAS PubMed Google Scholar
Gao, Y., Zhang, Y., Cao, Z., Guo, X., & Zhang, J. (2019). Decoding brain states from fMRI signals by using unsupervised domain adaptation. IEEE Journal of Biomedical and Health Informatics, 24(6), 1677–1685.
Gorgolewski, K., Burns, C. D., Madison, C., Clark, D., Halchenko, Y. O., Waskom, M. L., & Ghosh, S. S. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Frontiers in Neuroinformatics, 5, 12318.
Hebart, M. N., Dickter, A. H., Kidder, A., Kwok, W. Y., Corriveau, A., Van Wicklin, C., & Baker, C. I. (2019). THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images. PloS One, 14(10), e0223792.
Article CAS PubMed PubMed Central Google Scholar
Koizumi, A., Amano, K., Cortese, A., Shibata, K., Yoshida, W., Seymour, B., Kawato, M., & Lau, H. (2016). Fear reduction without fear through reinforcement of neural activity that bypasses conscious exposure. Nature Human Behaviour, 1(1), 0006.
Article PubMed PubMed Central Google Scholar
Li, X., Morgan, P. S., Ashburner, J., Smith, J., & Rorden, C. (2016). The first step for neuroimaging data analysis: DICOM to NIfTI conversion. Journal of Neuroscience Methods, 264, 47–56.
Li, Z., O’Doherty, J. E., Lebedev, M. A., & Nicolelis, M. A. (2011). Adaptive decoding for brain-machine interfaces through Bayesian parameter updates. Neural Computation, 23(12), 3162–3204.
Article PubMed PubMed Central Google Scholar
Margolles, P., Elosegi, P., Mei, N., & Soto, D. (2024). Unconscious manipulation of conceptual representations with decoded neurofeedback impacts search behavior. Journal of Neuroscience, 44(2).
Margolles, P., Mei, N., Elosegi, P., & Soto, D. (2023). PyDecNef: An open-source framework for fMRI-based decoded neurofeedback. 2023-10.
Mladenović, J., Mattout, J., and Lotte, F. (2018). A generic framework for adaptive EEG-based BCI training and operation. In Brain–Computer Interfaces Handbook (pp. 595-612). CRC Press.
Oakes, T. R., Johnstone, T., Walsh, K. O., Greischar, L. L., Alexander, A. L., Fox, A. S., & Davidson, R. J. (2005). Comparison of fMRI motion correction software tools. Neuroimage, 28(3), 529–543.
Article CAS PubMed Google Scholar
Orouji, S., Liu, M. C., Korem, T., & Peters, M. A. (2024). Domain adaptation in small-scale and heterogeneous biological datasets. Science Advances, 10(51), eadp6040.
Article PubMed PubMed Central Google Scholar
Perdikis, S., Leeb, R., & d R Millán, J. (2016). Context-aware adaptive spelling in motor imagery BCI. Journal of Neural Engineering, 13, 036018. https://doi.org/10.1088/1741-2560/13/3/036018
Perdikis, S., Tonin, L., Saeedi, S., Schneider, C., & Millán, J. D. R. (2018). The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users. PLoS Biology, 16(5), e2003787.
Article PubMed PubMed Central Google Scholar
Pinheiro, P.O. (2018). Unsupervised domain adaptation with similarity learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8004-8013).
Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PloS One, 10(3), e0118432.
Article PubMed PubMed Central Google Scholar
Shibata, K., Watanabe, T., Sasaki, Y., & Kawato, M. (2011). Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science, 334(6061), 1413–1415.
Article CAS PubMed PubMed Central Google Scholar
Shibata, K., Lisi, G., Cortese, A., Watanabe, T., Sasaki, Y., & Kawato, M. (2019). Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback. NeuroImage, 188, 539–556.
Sutton, R.S. (2018). Reinforcement learning: An introduction. A Bradford Book.
Vidaurre, C., Sannelli, C., & Blankertz, B. (2011). Machine-learning based co-adaptive calibration: towards a cure for BCI illiteracy. Neural Computation, 23, 791–816. https://doi.org/10.1162/NECO_a_00089
Vidaurre, C., Sannelli, C., Müller, K.-R., & Blankertz, B. (2011). Co-adaptive calibration to improve BCI efficiency. Journal of Neural Engineering, 8, 025009. https://doi.org/10.1088/1741-2560/8/2/025009
Vidaurre, C., Schlogl, A., Cabeza, R., Scherer, R., & Pfurtscheller, G. (2006). A fully on-line adaptive BCI. IEEE Transactions on Biomedical Engineering, 53(6), 1214–1219.
Article CAS PubMed Google Scholar
Watanabe, T., Sasaki, Y., Shibata, K., & Kawato, M. (2017). Advances in fMRI real-time neurofeedback. Trends in Cognitive Sciences, 21(12), 997–1010.
Article PubMed PubMed Central Google Scholar
Zhang, X., Chen, S., & Wang, Y. (2023). Kernel reinforcement learning-assisted adaptive decoder facilitates stable and continuous brain control tasks. IEEE Transactions on Neural Systems and Rehabilitation Engineering.
Zhang, S., Yoshida, W., Mano, H., Yanagisawa, T., Mancini, F., Shibata, K., Kawato, M., & Seymour, B. (2020). Pain control by co-adaptive learning in a brain-machine interface. Current Biology, 30(20), 3935–3944.
Zubarev, I., Zetter, R., Halme, H. L., & Parkkonen, L. (2019). Adaptive neural network classifier for decoding MEG signals. NeuroImage, 197, 425–434.
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