Progress in Machine Learning

Our team has developed sophisticated machine learning models to enable accurate and efficient detection of MCI and other neurological conditions.

• High Accuracy with Efficiency: One of our models is optimized for minimal computational requirements, enabling deployment on lightweight embedded devices for accessible and on-the-go screening.

• Advanced Temporal Analysis: Another model leverages advanced temporal dependencies to provide highly accurate diagnostics, evolving for deployment on a cloud platform. This approach ensures scalability and exceptional detection rates, empowering Neve 1.0 to support patients at scale.

Current Progress

• Prototyping: We have successfully designed the internal framework for Neve 1.0, sourced high-quality electrodes, and integrated machine learning capabilities for precise detection of neurological conditions.

• Collaborations: Working alongside academic partners, including the University of Lincoln, we continue to refine our algorithms and enhance the device’s capabilities.

• Clinical Vision: We aim to collaborate with the NHS and other healthcare providers to implement Neve 1.0 as a reliable screening tool for cognitive decline and a resource for mental health therapy.

• We are actively working on publishing a research paper to validate the efficacy and innovation of the Neve 1.0 headband, further demonstrating our commitment to advancing mental health technology.

Why Neve 1.0 Matters

As neurological conditions such as dementia and anxiety rise globally, early detection and intervention are critical. Neve 1.0 bridges the gap between advanced neuroscience and accessible healthcare, offering scalable, data-driven solutions to meet these challenges.

Join the Movement

Neve 1.0 is poised to transform personalized brain health care. Whether you’re a potential user, clinician, or investor, we welcome you to join us in shaping the future of mental health and cognitive wellness.

For inquiries or partnership opportunities, contact NeuroX today.

We are currently in the data collection stage, utilizing olfactory stimuli to further enhance the diagnostic capabilities of Neve 1.0. By integrating the analysis of brain responses to smell, we aim to uncover novel biomarkers for early detection of neurological conditions, such as mild cognitive impairment and dementia. This unique approach leverages the close connection between olfactory processing and cognitive function, adding an additional layer of precision and innovation to our machine learning models. These efforts are critical in advancing Neve 1.0 toward becoming a comprehensive tool for cognitive health assessment and intervention.