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Children with neurodevelopmental disorders require timely intervention to improve long-term outcomes, yet early screening remains inaccessible in many regions. A scalable solution integrating standardized assessments with physiological data collection, such as electroencephalogram (EEG) recordings, could enable early detection in routine settings by non-specialists. To address this, we introduce NeuroNest, a mobile and cloud-based platform for large-scale EEG data collection, neurodevelopmental screening, and research. We provide a comprehensive review of existing behavioral and biomarker-based approaches, consumer-grade EEG devices, and emerging machine learning techniques. NeuroNest integrates low-cost EEG devices with digital screening tools, establishing a scalable, open-source infrastructure for non-invasive data collection, automated analysis, and interoperability across diverse hardware. Beyond the system architecture and reference implementation, we highlight key challenges in EEG data standardization, device interoperability, and bridging behavioral and physiological assessments. Our findings emphasize the need for future research on standardized data exchange, algorithm validation, and ecosystem development to expand screening accessibility. By providing an extensible, open-source system, NeuroNest advances machine learning-based early detection while fostering collaboration in screening technologies, clinical applications, and public health.

Children with neurodevelopmental disorders require timely intervention to improve long-term outcomes, yet early screening remains inaccessible in many regions. A scalable solution integrating standardized assessments with physiological data collection, such as electroencephalogram (EEG) recordings, could enable early detection in routine settings by non-specialists. To address this, we introduce NeuroNest, a mobile and cloud-based platform for large-scale EEG data collection, neurodevelopmental screening, and research. We provide a comprehensive review of existing behavioral and biomarker-based approaches, consumer-grade EEG devices, and emerging machine learning techniques. NeuroNest integrates low-cost EEG devices with digital screening tools, establishing a scalable, open-source infrastructure for non-invasive data collection, automated analysis, and interoperability across diverse hardware. Beyond the system architecture and reference implementation, we highlight key challenges in EEG data standardization, device interoperability, and bridging behavioral and physiological assessments. Our findings emphasize the need for future research on standardized data exchange, algorithm validation, and ecosystem development to expand screening accessibility. By providing an extensible, open-source system, NeuroNest advances machine learning-based early detection while fostering collaboration in screening technologies, clinical applications, and public health.

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L-R: Representative of Ahaneku Chijioke, 1st runner-up, Ntapii Inc.; Founding Partner, Women & Careers and CEO NIGCOMSAT Limited, Mrs. Jane Ergerton-Aiden; First Prize Winner and Founder of VARLC Project, Charles Ogbodo; Project Director Women and Career, Emeka Amadi and Second Runner-up and Founder Neuronest Dr. Bunmi Alabi-Adebajo at the awards ceremony in Lagos recently Three Nigerian education technology (edtech) startups have received a combined N7 million in seed funding from NGO Women and Careers to scale innovative learning solutions that leverage AI and peer-to-peer teaching.

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Natalie Beazer’s NeuroNest initiative is reimagining disability care through community-centered housing, neurodiversity advocacy, and multi-generational support.

Natalie Beazer’s NeuroNest initiative is reimagining disability care through community-centered housing, neurodiversity advocacy, and multi-generational support.