Non-Invasive Detection and Preservation of Neurocognitive Signals in the Peri-Death Period Using Brain-Computer Interface and Artificial Intelligence

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).

NCT Number	NCT07477028
Lead Sponsor	Noah Tech, Corp. Collaborators: Columbia University, Massachusetts Institute of Technology, Stanford University, City of Hope Medical Center
Conditions	Terminal Illness, End-of-Life Care, Death, Brain Death, Death Anxiety, Consciousness, Electroencephalography, Gamma Oscillations +6 more
Enrollment	20 participants
Start Date	2026-09-01
Primary Completion	2030-09-30 (estimated)
Study Completion	2035-09-30 (estimated)
Updated on ClinicalTrials.gov	2026-03-17