Deep-tech Project aiming to significantly improve Neurofeedback Therapy
Neurofeedback
Neurofeedback is a noninvasive alternative to drug therapy targeting multiple health conditions, especially PTSD, depression, and ADHD. Next-generation neurofeedback, relying on precision medicine using fMRI and advanced computation, as well as home devices, opens the door to a new wave of adoption.
However, some pitfalls of the technology remain unaddressed. They cause inconsistency in response rates and a lack of interpretability, creating bottlenecks that hold back the industry from broad clinical and consumer adoption.
Solution
A new methodological software layer for neurofeedback therapy that directly addresses the aforementioned bottlenecks in the industry and opens new possibilities for personalization and objectivization.
A modality-agnostic framework designed to elevate current neurofeedback approaches.
Making use of advanced signal analysis and circuit mathematical modeling, combined with a patient-oriented approach targeting behavioral psychology and motor control, unified through causal inference.
Adam Jeřábek, MD
My goal is to apply deep electrotechnical knowledge of circuits and their analysis to the modeling of human cognition and mental and motor processes involving feedback loops. If there is a model, there is a prediction, if there is a prediction, there can be understanding. And only then can therapy be delivered effectively.
Story
My professional life is driven by a fascination with both medicine and technology and a passion for finding creative solutions to hard problems that can help others.
Medical experience
I received my MD from Charles University, after which I worked at the Neurology Department in an interventional neurology group as a resident neurologist and neuroscientist.
My work focused mainly on patients with Parkinson’s disease (PD). I gained experience in cutting-edge neurofeedback research at Maastricht University and focused my own research on fMRI neurofeedback in patients with PD.
Overall, my medical background gave me a unique and intuitive understanding of biological feedback loops, since the reflex arc and its examination are both a fundamental concept and a focal point of neurology.
Electrical engineering
I completed both my bachelor’s and master’s degrees in electrical engineering, specializing in signal processing and analysis, at the Czech Technical University in Prague.
In my thesis, I focused on the analysis, interpretation, and fusion of EMG and DBS signals, as well as on the fusion of simultaneous fMRI and EEG data.
Overall, my electrical engineering background gave me a strong foundation in closed-loop feedback systems, as they are a core element of signal modeling and analysis.