Neural-Plasma Algorithm for Non-Invasive Signal Detection and Opt-In Addiction Monitoring via CubeSat Constellation

This preprint presents a non-invasive, opt-in CubeSat-based neural monitoring system that specifically detects dopamine reward-system hyperactivity and compulsive engagement patterns linked to both substance and behavioral addictions. The architecture integrates real-time neural signal processing with voluntary genetic screening and quantum-biological insights (electron transfer, dissipative quantum systems, coherent biophoton emissions). Personalized feedback follows Knowles’ andragogical principles to empower adult users with self-directed guidance. A tiered intervention protocol (feedback → limits → suspension → removal) gives platforms a proactive, consent-based safeguard that demonstrates reasonable care while respecting adult autonomy. The paper addresses societal responsibility in the digital age: marketing psychology tactics trace back to John B. Watson’s 1920s behaviorist work (emotional targeting, associative conditioning, the “coffee break” campaign). If these long-established techniques are deemed inherently “bad,” entire marketing degree programs in business schools would logically need to be eliminated—an untenable position. Rather than blanket blame on platforms like Meta and Google, the system equips companies with precise tools to detect and respond to known addictive mechanisms while treating users as autonomous adults.