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).
Adults ≥18 years with terminal illness or severe acute trauma
Do-not-resuscitate (DNR/DNI) order in place
Surrogate decision-maker available and willing to provide informed consent
Expected survival ≤7 days (physician estimate)
Brain death already declared > 24 hours prior to enrollment
Contraindication to EEG/BCI headset placement (e.g., severe scalp injury)
Patient lacks a legally authorized representative
Noah Tech, Corp.
Lead sponsor
Columbia University
Collaborator
Massachusetts Institute of Technology
Collaborator
Stanford University
Collaborator
City of Hope Medical Center
Collaborator