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

• Autor(a) principal: Costa, Nuno M. C. da
• Data de Publicação: 2021
• Outros Autores: Bicho, Estela, Dias, Nuno S.
• Idioma: eng
• Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Texto Completo: https://hdl.handle.net/1822/90176
• topic: Self-regulation; Assisted neurofeedback; Neurostimulation; Mindfulness; Open-source BCI; Machine learning; Ciências Médicas::Biotecnologia Médica; Saúde de qualidade

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.