EEG To FMRI Synthesis: Is Deep Learning a Candidate?

Advances on signal, image and video generation underly major breakthroughs on generative medical imaging tasks, including Brain Image Synthesis. Still, the extent to which functional Magnetic Ressonance Imaging (fMRI) can be mapped from the brain electrophysiology remains largely unexplored. This work provides the first comprehensive view on how to use state-of-the-art principles from Neural Processing to synthesize fMRI data from electroencephalographic (EEG) data. Given the distinct spatiotemporal nature of haemodynamic and electrophysiological signals, this problem is formulated as the task of learning a mapping function between multivariate time series with highly dissimilar structures. A comparison of state-of-the-art synthesis approaches, including Autoencoders, Generative Adversarial Networks and Pairwise Learning, is undertaken. Results highlight the feasibility of EEG to fMRI brain image mappings, pinpointing the role of current advances in Machine Learning and showing the relevance of upcoming contributions to further improve performance. EEG to fMRI synthesis offers a way to enhance and augment brain image data, and guarantee access to more affordable, portable and long-lasting protocols of brain activity monitoring. The code used in this manuscript is available in Github and the datasets are open source

NetLangEd, A Web Editor to Support Online Comment Annotation

This paper focuses on the scientific areas of Digital Humanities, Social Networks and Inappropriate Social Discourse. The main objective of this research project is the development of an editor that allows researchers in the human and social sciences or psychologists to add their reflections or ideas out coming from reading and analyzing posts and comments of an online corpus . In the present context, the editor is being integrated with the analysis tools available in the NetLang platform. NetLangEd, in addition to allowing the three basic operations of adding, editing and removing annotations, will also offer mechanisms to manage, organize, view and locate annotations, all of which will be performed in an easy, fast and user-friendly way

Fractional Order Processing of Satellite Images

Nowadays, satellite images are used in many applications, and their automatic processing is vital. Conventional integer grey-scale edge detection algorithms are often used for this. This study shows that the use of color-based, fractional order edge detection may enhance the results obtained using conventional techniques in satellite images. It also shows that it is possible to find a fixed set of parameters, allowing automatic detection while maintaining high performance