User friendly knowledge acquisition system for medical devices actuation

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaInternet provides a new environment to develop a variety of applications. Hence, large amounts of data, increasing every day, are stored and transferred through the internet. These data are normally weakly structured making information disperse, uncorrelated, non-transparent and difficult to access and share. Semantic Web, proposed by theWorldWideWeb Consortium (W3C), addresses this problem by promoting semantic structured data, like ontologies, enabling machines to perform more work involved in finding, combining, and acting upon information on theWeb. Pursuing this vision, a Knowledge Acquisition System (KAS) was created, written in JavaScript using JavaScript Object Notation (JSON) as the data structure and JSON Schema to define that structure. It grants new ways to acquire and store knowledge semantically structured and human readable. Plus, structuring data with a Schema generates a software robust and error – free. A novel Human Computer Interaction (HCI) framework was constructed employing this KAS, allowing the end user to configure and control medical devices. To demonstrate the potential of this tool, we present the configuration and control of an electrostimulator. Nowadays, most of the software for Electrostimulation is made with specific purposes, and in some cases they have complicated user interfaces and large, bulky designs that deter usability and acceptability. The HCI concedes the opportunity to configure and control an electrostimulator that surpasses the specific use of several electrostimulator software. In the configuration the user is able to compile different types of electrical impulses (modes) in a temporal session, automating the control, making it simple and user-friendly

NeuroPrime: a Pythonic framework for the priming of brain states in self-regulation protocols

Due to the recent pandemic and a general boom in technology, we are facing more and more threats of isolation, depression, fear, overload of information, between others. In turn, these affect our Self, psychologically and physically. Therefore, new tools are required to assist the regulation of this unregulated Self to a more personalized, optimal and healthy Self. As such, we developed a Pythonic open-source humancomputer framework for assisted priming of subjects to “optimally” self-regulate their Neurofeedback (NF) with external stimulation, like guided mindfulness. For this, we did a three-part study in which: 1) we defined the foundations of the framework and its design for priming subjects to self-regulate their NF, 2) developed an open-source version of the framework in Python, NeuroPrime, for utility, expandability and reusability, and 3) we tested the framework in neurofeedback priming versus no-priming conditions. NeuroPrime is a research toolbox developed for the simple and fast integration of advanced online closed-loop applications. More specifically, it was validated and tuned for the research of priming brain states in an EEG neurofeedback setup. In this paper, we will explain the key aspects of the priming framework, the NeuroPrime software developed, the design decisions and demonstrate/validate the use of our toolbox by presenting use cases of priming brain states during a neurofeedback setup.MIT -Massachusetts Institute of Technology(PD/BD/114033/2015

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements