Personalized home based neurostimulation via AI optimization augments sustained attention

Journal article

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.

Authors: Cohen Kadosh, R; Ciobotaru, D; Karstens, MI; Nguyen, V

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: npj Digital Medicine
• Volume: 8
• Issue: 1
• Article number: 463
• Publication date: 2025-07-29
• Acceptance date: 2025-05-20
• DOI: 10.1038/s41746-025-01744-6
• EISSN: 2398-6352
• ISSN: 2398-6352
• Language: English
• Keywords: Neurostimulation; Computer science; Neuroscience; Medicine; Psychology; Stimulation