Mind Brain Insight, LLC is a small consulting company working in applied neuroscience.
Our short term goal is to enhance specific BCI (brain computer interface) projects; such as motor neuroprosthetics and other mind-controlled devices.
Our long term goal is to lay the foundation for revolutionary advances in applied neuroscience: from CNS medicine — biomarkers, therapeutic target ID, and clinical treatment — to AGI, NLP, knowledge representation, cognitive computing, and bio-inspired robotics.
MBI is based on a new cognitive neuroscience paradigm — a new way to connect the mind to the brain — called the MA (Memory Activation) Method. The method defines mental processes and the mind (recognition, meaning, thought, imagination, executive control, goals, intention…) as active memory sets; lists, connects and weighs these; and maps them to the brain’s functional neural networks, and brain signal.
A more accurate definition of mind allows for more accurate mapping to the brain.
The MA Method allows enables moving beyond task-based mapping, to mind-based mapping. The latter is significantly more accurate, and useful, than the former. Simple experiments can prove this.
The MA Method, I claim, can quickly and dramatically enhance most (legitimate) BCI projects. This includes device design, testing, learning, user satisfaction, and everyday use. For a demonstration, see the Mind Mapping Challenge.
Relevant Links: Defining Brain Signal “Targets” for BCI Control
Despite lacking a (clear and accurate) mind/brain paradigm, there are many researchers doing amazing work in furthering the brain sciences. Some examples relevant to my work:
Bressler, Steven L., Kelso, J.A. Scott (2016). Coordination dynamics in cognitive neuroscience. Frontiers in Neuroscience, 10, 397. https://www.frontiersin.org/articles/10.3389/fnins.2016.00397/full
Fingelkurts, Andrew A., Fingelkurts, Alexander A., Neves, Carlos F. H. (2009). Phenomenological architecture of a mind and operational architectonics of the brain: the unified metastable continuum. New Mathematics and Natural Computation, 5, 221-244. https://www.bm-science.com/images/bms/publ/art53.pdf
Fuster, Joaquin M. (2006). The cognit: A network model of cortical representation. International Journal of Psychophysiology, 60, 125-32. https://www.researchgate.net/publication/7153374_The_cognit_A_network_model_of_cortical_representation
Poldrack, Russell. A., Yarkoni, Tal. (2016). From brain maps to cognitive ontologies: informatics and the search for mental structure. Annual Review of Psychology, 67, 587. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701616/