Brain Death, Cognition, Cognitive, Consciousness, Death, Death Anxiety, EEG, Electroencephalography, End-of-Life Care, Gamma Oscillations, Memory, Near-Death Phenomena, Severe Acute Trauma, Terminal Illness
Conditions
Keywords
AI, BCI, Brain Computer Interface, Death, Gamma Oscillations, Cognition, Consciousness, Pefi-death, Brain death, Reservation, Memory, Life, Life experience, Transfer, Convergence
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.
Interventions
None listed
Sponsors
Noah Tech, Corp.
Columbia University
Massachusetts Institute of Technology (MIT)
Stanford University
City of Hope Medical Center
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Adults ≥18 years with terminal illness or severe acute trauma 2. Do-not-resuscitate (DNR/DNI) order in place 3. Surrogate decision-maker available and willing to provide informed consent 4. Expected survival ≤7 days (physician estimate)
Exclusion criteria
1. Brain death already declared \> 24 hours prior to enrollment 2. Contraindication to EEG/BCI headset placement (e.g., severe scalp injury) 3. Patient lacks a legally authorized representative
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Detection of Neurocognitive Signals After Clinical Death Prior to Brain Death | 0-120 minutes after clinical death | Presence or absence of organized neurocognitive signals and measurable brain activity, as recorded by non-invasive high-density electroencephalography (EEG), in the human brain during the period immediately following clinical death (cessation of circulation) but prior to declaration of brain death. The primary outcome will be reported as the proportion of participants with detectable organized neural activity (yes/no) meeting predefined signal thresholds. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Successful Capture and Preservation of Neurocognitive Signals | Up to 24 hours after clinical death | Proportion of participants for whom real-time neurocognitive signals were successfully captured, recorded in high quality, and securely preserved for long-term storage using non-invasive high-density EEG and wireless brain-computer interface (BCI) technology. Reported as the number and percentage of participants with complete, artifact-free recordings suitable for analysis. |
| Functional Connectivity Patterns | Up to 24 hours after clinical death | Quantitative analysis of functional connectivity patterns among brain regions, measured using coherence and phase-locking value (PLV) indices, reported on a scale from 0 (no connectivity) to 1 (perfect connectivity). |
| Cross-Frequency Coupling | Up to 24 hours after clinical death | Quantitative analysis of cross-frequency coupling between EEG frequency bands, measured using the modulation index (MI), reported as a unitless value ranging from 0 (no coupling) to 1 (maximum coupling). |
| Temporal Dynamics of Neurocognitive Signals | Up to 24 hours after clinical death | Quantitative analysis of the temporal dynamics of neurocognitive signals, including signal duration and onset latency, measured in seconds (s) from the time of clinical death. |
| Informational Content of Neurocognitive Signals | Up to 24 hours after clinical death | Qualitative and quantitative analysis of the potential informational content of captured neurocognitive signals, measured using permutation entropy, reported as a unitless value on a scale from 0 (completely regular/predictable) to 1 (completely random). |
| Spectral Power of Neurocognitive Signals | Up to 24 hours after clinical death | Quantitative analysis of spectral power of captured neurocognitive signals, measured in microvolts squared per hertz (µV²/Hz), across standard EEG frequency bands (delta, theta, alpha, beta, gamma). |
Contacts
CONTACTDr. Wallace Lynch, Ph.D.
Outcome results
None listed