Spatially Masked Regression Reveals Local and Distributed Predictability in Electrophysiological Recordings

2026-06-09

AI Summary

This study investigates the extent to which single-electrode signals in electrophysiological recordings reflect local neighboring versus globally distributed neural activity. To this end, the authors propose a Spatial Masked Regression (SMR) framework that systematically excludes signals from configurable neighborhoods around a target electrode to reconstruct its time series, thereby treating spatial neighborhood as a controlled variable to quantify the relative contributions of local redundancy and global structure. Applying SMR to both intracranial and scalp EEG data—supplemented by control analyses involving distance correlations and disruptions of phase or temporal structure—the study finds that signals remain significantly predictable even after excluding nearby electrodes. Although neighboring channels contribute substantially, they are not the sole source of predictability, and SMR performance relies on temporal and cross-channel structure rather than marginal statistics. These results reveal a strong global component in EEG signals with high cross-subject generalizability.

Abstract

Neural recordings are often interpreted as local measurements, yet the signal at any one sensor can also reflect structured activity distributed across the broader network. This raises a basic question: to what extent does an electrode's signal reflect local versus distributed information in the underlying system? More specifically, how much of an electrode's activity is carried by its immediate neighborhood, and how much is embedded more broadly across the array? We address this with a Spatially Masked Regression (SMR) framework that reconstructs each electrode's timeseries from the remaining electrodes while excluding a configurable neighborhood around the target. By progressively increasing this mask, spatial locality becomes an experimental control for quantifying how much predictive information survives after nearby channels are withheld. We apply SMR to intracranial EEG with heterogeneous electrode coverage and to scalp EEG with standardized montages over sensorimotor cortex. Using distance correlation between original and reconstructed signals, we find strong within-subject reconstruction in both modalities, substantial residual predictability even when local neighbors are excluded, and markedly stronger cross-subject transfer in EEG than in iEEG. Masking shows that nearby electrodes contribute strongly to reconstruction but do not account for all of it, indicating that individual channels reflect both local redundancy and broader distributed structure. Surrogates that preserve selected marginal or spectral properties while disrupting phase structure or temporal ordering substantially reduce performance, supporting the conclusion that SMR depends on structured temporal and cross-channel organization rather than on marginal statistics alone. These results position SMR as an interpretable framework for quantifying the balance between local and distributed information in recordings.

Problem

• locality
• distributed information
• electrophysiological recordings
• spatial predictability
• neural signals

Innovation

• Spatially Masked Regression
• distributed neural activity
• local vs. global predictability
• electrophysiological recordings
• cross-channel reconstruction