Non-Invasive Detection and Preservation of Neurocognitive Signals in the Peri-Death Period Using Brain-Computer Interface and Artificial Intelligence
brief summary
Background: Recent electroencephalography (EEG) data indicate that the transition from clinical death to cellular death is marked by highly organized neurophysiological events, including significant surges in gamma-band power, cross-frequency coupling, and distinct spreading depolarization waves. This prospective, observational feasibility study utilizes rapid-deployment, high-density, noninvasive BCI hardware paired with proprietary AI analytics to detect, classify, and securely archive these terminal neurocognitive signals. Objectives: (1) Quantify transient gamma-band activity and cross-frequency connectivity post-clinical death; (2) Validate the efficacy of machine learning models for real-time signal classification in high-noise clinical environments; (3) Establish a highly secure, encrypted bio-informational archive of peri-life EEG data. Design: Prospective, open-label, multicenter, observational cohort (n\>20).
detailed description
This prospective observational feasibility study will use non-invasive high-density EEG combined with a wireless brain-computer interface (BCI) and artificial intelligence analytics to detect, characterize, and archive neurocognitive signals in adult patients during the peri-death period. The study includes individuals with terminal illness or severe acute trauma who have a do-not-resuscitate (DNR/DNI) order. Building on recent human findings of gamma oscillation surges and cross-frequency coupling (Vicente et al., 2022; Xu et al., 2023), the study aims to quantify these signals, test AI-driven real-time classification, and explore technical feasibility for future signal preservation and continuity research. No therapeutic intervention is performed. All monitoring is conducted with surrogate consent under strict ethical oversight.
official title
Feasibility of Non-Invasive Detection and Preservation of Neurocognitive Signals in the Peri-Death Period Using Brain-Computer Interface and Artificial Intelligence: A Prospective Observational Study (NeuroCogPresv)