Surrogate controls showed that SMR was not simply exploiting marginal statistics.
Phase-shuffled, IAAFT, and block-shuffled surrogates all substantially reduced performance.
So reconstruction depends on structured temporal and cross-channel organization, not just spectra or amplitudes.
6/n 🧵👇
If reconstruction collapses when nearby electrodes are removed, the signal is mostly locally redundant.
If reconstruction survives local masking, then some information about that target is distributed across more distant parts of the array.
4/n 🧵👇
arxiv.org/html/2606.11...
The usual approach is functional connectivity: correlation, coherence, PLV, mutual information, graph edges, etc.
These methods ask whether two signals are statistically related.
SMR asks a different question:
Can one electrode’s signal be reconstructed from the rest of the array?
2/n 🧵👇
arxiv.org
As local neighborhoods were progressively excluded, performance decreased.
So local electrodes carry the strongest predictive information.
But reconstruction remained above zero even under strict local masking.
The signal is locally anchored, but not purely local.
5/n 🧵👇
In this paper we introduce “Spatially Masked Regression” (SMR) as a reconstruction-based framework for separating local redundancy from distributed predictability in electrophysiological recordings. I...
The key move is spatial masking.
For each target electrode, we reconstruct its time series from other electrodes while progressively excluding its local neighborhood.
This turns “locality” into an experimental knob.
3/n 🧵👇
neurovium.science/posts/pblog-...