Information Capacity of EEG: Theoretical and Computational Limits of Recoverable Neural Information

TL;DR

This paper quantifies the maximum neural information that EEG can reliably transmit, revealing fundamental limits imposed by physics and noise, and showing that current decoding methods are far from these theoretical bounds.

Contribution

It combines information theory and modeling to estimate EEG's information capacity and identifies the physical and noise constraints limiting neural data recovery.

Findings

EEG conveys only tens of bits per sample about neural activity.

Information saturates with 64-128 electrodes and scales logarithmically with SNR.

Linear decoders recover nearly all linearly recoverable variance but fall short of channel capacity.

Abstract

Electroencephalography (EEG) is widely used to study human brain dynamics, yet its quantitative information capacity remains unclear. Here, we combine information theory and synthetic forward modeling to estimate the mutual information between latent cortical sources and EEG recordings. Using Gaussian-channel theory and empirical simulations, we find that scalp EEG conveys only tens of bits per sample about low-dimensional neural activity. Information saturates with approximately 64-128 electrodes and scales logarithmically with signal-to-noise ratio (SNR). Linear decoders capture nearly all variance that is linearly recoverable, but the mutual information they recover remains far below the analytic channel capacity, indicating that measurement physics - not algorithmic complexity - is the dominant limitation. These results outline the intrinsic ceiling on how much structure about brain state or thought content can be inferred from EEG.