Abstract
BackgroundNewly emerged developments in decoding of stimulus images from fMRI measurements have shown promising results. Decoding-classification has been the main concern of decoding studies, whereas the matter of reconstruction (decoding) of stimulus images from fMRI data, especially natural images, lacks adequate examination and it requires plenty of efforts to improve. New methodThe present study employs Bayesian networks for decoding-reconstruction which is a novel application of this tool. Moreover, as a novel approach, we exploit the brain connectivity information in decoding-reconstruction procedure through Bayesian networks. ResultsThe proposed method was applied to reconstruct 100 images of digits 6 and 9 from the fMRI measurements obtained when showing some handwritten images of 6 and 9 to the subject. The information of only 10 brain voxels were exploited and an average (standard deviation) city-block distance error of 0.1071(0.0134) was obtained for all stimuli's reconstruction.In comparison with current common methods: The results reveal that Bayesian networks are successful in decoding-reconstruction of handwritten digits and inclusion of brain connectivity information makes them perform even more efficiently and improves decoding-reconstruction as well (reducing average error by almost 5%). ConclusionIn the task of decoding-reconstruction, the models including brain connectivity appear significantly superior to other existing models.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.