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