Personalized home based neurostimulation via AI optimization augments sustained attention
Abstract Brain-based technologies for human augmentation face challenges in personalization and real-world translation. We present an AI-driven personalized Bayesian optimization algorithm that remotely adjusts neurostimulation parameters based on baseline ability and head anatomy to enhance sustain...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01744-6 |
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| Summary: | Abstract Brain-based technologies for human augmentation face challenges in personalization and real-world translation. We present an AI-driven personalized Bayesian optimization algorithm that remotely adjusts neurostimulation parameters based on baseline ability and head anatomy to enhance sustained attention at home. Validated through in silico modeling and a double-blind, sham-controlled study, our approach aligns with MRI-based models and neurobiological theories, maximizing efficacy and enabling scalable, personalized cognitive enhancement and therapy in real-world settings. |
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| ISSN: | 2398-6352 |