Source Estimation

Topographic maps showing how μECoG at Motor Cortex projects across the scalp

Simulating Scalp EEG from Ultrahigh-Density ECoG Data Illustrates Cortex to Scalp Projection Patterns

Using 1024-electrode ultrahigh-density electrocorticography (μECoG) data as ground truth, this study demonstrates that cortical activity from a small 3×3 cm patch projects broadly across the entire scalp surface, not just to nearby EEG electrodes. By applying ICA decomposition and forward-projecting through a high-definition head model, we show that scalp EEG channels reflect complex mixtures of distributed cortical sources rather than primarily local activity. These findings challenge conventional channel-level EEG interpretation approaches and underscore the critical importance of source-level analysis methods for accurate understanding of brain electrical activity.

The effect of fiducial mismarking on EEG source estimation.

Nonlinear functional muscle network based on information theory tracks sensorimotor integration post stroke

We show that InfoMuNet, a novel functional biomarker based on a nonlinear network graph of muscle connectivity, can quantify the role of sensory information on motor performance. We demonstrate its potential use in precision rehabilitation interventions.

Differential Theta-Band Signatures of the Anterior Cingulate and Motor Cortices During Seated Locomotor Perturbations

We demonstrate that seated locomotor perturbations produce differential theta-band responses in the anterior cingulate and supplementary motor areas, suggesting that tuning perturbation parameters can potentially modify electrocortical responses.

Re-referencing EEG data will delete common mode biological and non-biological signals.

EEG Re-refrencing Methods, Why and How?

In exploring EEG re-referencing techniques, it’s emphasized that re-referencing may unintentionally remove common mode biological signals, crucial for accurate data interpretation. This document from the BRaIN Lab at the University of Central Florida discusses the implications of such data loss and proposes methods to mitigate these effects, ensuring reliable EEG analysis.

The five digitizing methods tested in this study.

More Reliable EEG Electrode Digitizing Methods Can Reduce Source Estimation Uncertainty, but Current Methods Already Accurately Identify Brodmann Areas

Download Paper Code and data Abstract Electroencephalography (EEG) and source estimation can be used to identify brain areas activated during a task, which could offer greater insight on cortical dynamics. Source estimation requires knowledge of the locations of the EEG electrodes. This could be provided with a template or obtained by digitizing the EEG electrode locations. Operator skill and inherent uncertainties of a digitizing system likely produce a range of digitization reliabilities, which could affect source estimation and the interpretation of the estimated source locations....

Influence of Mismarking Fiducial Locations on EEG Source Estimation

Mismarking fiducial locations can systematically change EEG source locations. We inestigated this effect by systematically moving the fiducial locations to simulate such errors.