The Multitasking Brain Under AI Collaboration: An Exploratory N=1 EEG Study During Concurrent Theory Construction

Background: Theta-gamma cross-frequency coupling is a well-established neural mechanism for memory retrieval and cognitive binding, but has never been studied during naturalistic, AI-assisted creative work. The "neural efficiency" hypothesis suggests that high-performing individuals show reduced overall brain activation, yet detailed characterization of extreme cognitive performers remains rare.Objective: This exploratory N=1 study documents EEG patterns during a novel task paradigm: concurrent engagement with two AI systems (Claude and Gemini) for simultaneous philosophy paper writing and real-time brain data analysis.Methods: A twice-exceptional adult male (WAIS-IV GAI: 126, VCI: 136; author of 200+ academic papers in two months) recorded 42 minutes of 4-channel EEG (Muse S) while multitasking with two AI assistants. Data were analyzed for cross-frequency correlations, hemispheric asymmetry, and artifact rejection.Results: The right temporal channel (TP10) showed sustained elevated theta power (M = 1.48 dB, compared to 0.75, 0.00, and -0.03 dB for other channels) with an unusually high theta-gamma correlation (r = 0.895). Critically, theta-delta correlation was near zero (r = 0.043), ruling out movement artifact as an explanation. Heart rate remained stable (M = 72.4 bpm), indicating a relaxed physiological state.Interpretation: Four competing hypotheses are presented: (A) transformed Default Mode Network function, (B) right-temporal specialization for conceptual processing, (C) theta-gamma coupling as "download" mode enabling rapid memory-to-output conversion, and (D) electrode placement artifact. This study does not adjudicate between these hypotheses but demonstrates their testability.Conclusion: This is the first documented case of brain activity during human-AI collaborative theory construction. The observation generates testable hypotheses about high-productivity cognition and the potential neural effects of AI collaboration.