Riding Brainwaves in LLM Space: Understanding Activation Patterns Using Individual Neural Signatures

TL;DR

This study demonstrates that frozen large language models encode stable, person-specific neural signals in their deep layers, enabling EEG-based individualization of model responses with high accuracy.

Contribution

It reveals that frozen LLMs contain stable, person-specific neural directions, and introduces linear probes to decode individual EEG signals from model activations.

Findings

Person-specific probes outperform population probes across EEG features.

High-gamma power prediction achieves rho = 0.183 with person-specific probes.

Person-specific signals are stable over time and localized in deep model layers.

Abstract

Consumer-grade EEG is entering everyday devices, from earbuds to headbands, raising the question of whether language models can be adapted to individual neural responses. We test this by asking whether frozen LLM representations encode person-specific EEG signals, directions in activation space that predict one person's brain activity but not another's. Using word-level EEG from 30 participants reading naturalistic sentences (ZuCo corpus), we train a separate linear probe for each person, mapping hidden states from a frozen Qwen 2.5 7B to that individual's EEG power. Person-specific probes outperform a single population probe on every EEG feature tested; for high-gamma power, the person-specific probe achieves rho = 0.183, a ninefold improvement over the population probe (rho = 0.020, p < 10^-4). A negative control, fixation count, shows no person-specific advantage (p = 0.360); fixation count reflects word length and frequency rather than individual cognition. The individual directions are temporally stable (split-half cosine = 0.824), non-transferable across people (self rho = 0.369 vs. other rho = 0.143, p < 10^-19), and distinct from the shared population signal: person-specific probes retain predictive power after the population component is removed. The person-specific signal concentrates in the model's deep layers, rising consistently with depth and peaking at Layer 24 of 28. The results are consistent across architectures (LLaMA 3.1 8B) and survive word-level confound controls. Frozen language models contain stable, person-specific neural directions in their deep layers, providing a geometric foundation for EEG-driven personalization.