The Resonance Framework: A Phased Neural-Interface Paradigm for Cognitive Motor Dissociation in Anoxic Brain Injury
Abstract
Up to 25% of patients who survive severe anoxic brain injury (such as from cardiac arrest) are trapped in Cognitive Motor Dissociation (CMD)—meaning they are consciously aware but completely unable to move. A tragic clinical blind spot occurs when these patients are misdiagnosed with classic Locked-In Syndrome and given eye-tracking devices to communicate. Because anoxic injury destroys the specific brain networks required for voluntary eye movement, these patients inevitably fail the tests. They are then falsely labeled as unconscious, which frequently leads to the premature withdrawal of life-sustaining care.To address this diagnostic failure, this paper introduces “Track 3: The Resonance Framework.” Instead of relying on broken motor or eye pathways, this framework uses direct neural receivers (OPM-MEG quantum sensors) and active mental imagery to detect covert consciousness. Once awareness is confirmed, the protocol uses non-invasive neuromodulation—specifically thalamic ultrasound (LIFUP) and 40Hz gamma entrainment—to clear “cortical idling” and stabilize the patient’s arousal networks.Finally, the framework uses an AI-mediated Brain-Computer Interface (BCI) to translate the patient’s neural intent into communication. The entire clinical progression is managed by a computable quantum optimization (QUBO) model to ensure patient safety. Guided by the “Communication-First Principle” and lived-experience advocacy, the ultimate goal of this framework is to restore autonomy. It provides a biologically realistic pathway for patients to either regain control over their environment or, if strict accuracy thresholds are met, establish the legal capacity to make their own end-of-life medical decisions.
Fields of Study & Subjects
Traumatic Brain Injury Research
EEG and Brain-Computer Interfaces
Stroke Rehabilitation and Recovery
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 25 February 2026
- Language
- EN
- Document type
- Preprint