Neuro-Cognitive AI Fingerprinting for Pre-Radiographic Brain Tumor Detection Using Transformer-Based Phase-Amplitude Coupling Analysis

Conventional brain tumor diagnosis waits for a mass to grow large enough to show up on an MRI. By that point, the damage is already done — treatment windows are narrower, and survival outcomes are far worse than they needed to be. This work takes a different angle. Rather than searching for a physical lump, we ask a more fundamental question: what does a growing tumor do to the brain's electrical behavior, and can an AI system learn to recognize that disruption early? Our method is built around Phase-Amplitude Coupling, or PAC — a measurable property of how high- and low-frequency brain waves coordinate with each other. Tumor-induced inflammation disrupts this coordination well before anything appears on structural imaging. We trained a Transformer-based deep learning model on synthetic EEG signals from 900 virtual subjects, paired with three behavioral biomarker features: eye-tracking variance, gait asymmetry, and speech syntax irregularity. On the held-out test set, the model reached 87.4% classification accuracy and an AUC-ROC of 0.93 for the highest-risk class. Attention analysis showed the model learned to focus on theta-gamma PAC features across frontal and temporal channels — precisely where neuroscience literature would predict disruption to appear. The results demonstrate that this detection framework is computationally viable, even if clinical validation lies ahead.