AlgiSense is developing EEG-based digital biomarkers to objectively detect and monitor chronic pain using graph-based AI. A significant technical challenge in EEG research and development is harmonizing feature data from various electrode configurations across research and consumer-grade devices. In this lightning talk, we will explore how Neo4j enables a dynamic and interoperable graph schema that reconciles these configurations through spatial interpolation and graph-based topology mapping, supporting GNN training and inference even when the data includes missing or sparse channels. We utilize Neo4j’s Cypher to implement a scalable interpolation strategy for sensor data, enabling adaptive learning in edge-to-cloud deployments. We will examine how our approach supports low-resource inference, powers personalized pain assessments, and paves the way for foundational graph models in clinical neuroinformatics. This talk will appeal to developers working on bioinformatics, health tech, edge AI, or real-world applications.
Speaker
Zeyno Dodd
Co-Founder and R&D Tech Lead, AlgiSense LLC
Zeyno Dodd is the R&D lead at AlgiSense, a technology startup advancing AI-driven digital biomarkers for pain detection and monitoring. Her work focuses on ethical uses of AI, Graph Neural Networks (GNNs), cloud and edge ML computing, and scalable AI model deployment. Zeyno brings expertise in bridging biomedical sensor data with GNNs for real-time inference on edge devices and has recently led efforts in developing agentic workflows in support of evidence-based medicine applications. She is passionate about building responsible, high-impact AI systems that bridge human data and machine learning insight.
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