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Exploring the Depths of Human Consciousness


Our Current Focus

We are collaborating with the Human-AI Empowerment Lab at Clemson University in the United States, with the common goal to leverage the power of Machine Learning in the understanding of brain activity. We are also working with the Grinberg Research Centre and ADA Centro de Luz in Mexico, developing integrative biofeedback programs for the general population.

Scanning Headset

Pioneering Consciousness Research

Interdisciplinary Collaboration


Our team of experts from diverse fields work together to explore the intricate relationship between the mind, the brain, and the human experience.

Cutting-Edge Technology


We are at the forefront of developing and refining advanced Biofeedback techniques, utilizing the latest neuroimaging technology and novel data analysis methods to push the boundaries of cognitive studies.

Understanding the Mind-Body Connection

Neural Pathways

By studying the complex neural networks and their intricate connections, we gain a deeper understanding of how the brain processes information, regulates emotions, and shapes the human experience.

Consciousness Dynamics


We delve into the dynamic interplay between conscious and subconscious processes, examining how these complex interactions influence our decision-making, problem-solving, and overall sense of self.

Brain Scans
Image by Hal Gatewood

Biofeedback for Self-Empowerment

Personalized Insights


Our Biofeedback technology provide real-time data about your physiological and neurological responses, empowering the individual to gain a deeper understanding of their own mind-body connection.

Enhancing Self-Awareness


By learning to consciously regulate your physical and mental states, you can develop greater self-awareness, improved emotional regulation, and enhanced overall well-being.

Abstract Hashtag


Neuroimaging and Machine Learning for Enhancing Functional Recovery

Collaborating Institutions:





This research outlines a collaborative effort between the above stated research institutions to investigate the potential of advanced neuroimaging techniques and machine learning algorithms in understanding and enhancing recovery processes in individuals with functional disabilities.


Specifically, the project explores the analysis of brain signals and biomechanics. By combining the medical expertise and resources of CHU de Reims with the computational and analytical capabilities of Clemson University and the Metacognition Institute biometric and biofeedback research, we aim to develop innovative interventions that can improve the lives of those facing neurological challenges.

Understanding the limits of LLMs through an in depth analysis of human language. 

Collaborating Institutions:


We are designing a set of tests to understand the logical and reasoning limits of current LLMs. By carefully analysing the roots of language and its grounding, we aim to provide a framework to help us understand the role of language in the human scene.

Our approach involves dissecting the foundational aspects of language, including syntax, semantics, and pragmatics, to pinpoint where LLMs excel and where they fall short. By creating a comprehensive suite of benchmarks that reflect the complexity and nuances of human communication, we can better assess the capabilities and limitations of these models. This analysis will not only highlight the gaps in current LLMs but also guide future research and development towards more robust and human-like language processing systems.

Furthermore, we will explore how language is intertwined with human cognition and social interaction. Understanding the contextual and cultural elements that influence language use can reveal how LLMs interpret and generate text. This insight is crucial for developing LLMs that can navigate the intricacies of human language with greater accuracy and empathy. Our ultimate goal is to bridge the gap between artificial and human intelligence, fostering more meaningful and effective communication between humans and machines.

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