What are the units of the time series after source reconstruction

[Raymundo.Cassani]

@YYingH1213

In this case, can we directly use the EEG and EMG signals after source reconstruction for coupling analysis (such as coherence, Granger causality, and phase-locked value)?

It seems there is confusion on the nature of the signals.

EEG signals capture the difference of potential (voltage) between two points on the scalp, and it is measured in [Volt]. These signals are in the sensor space (so there is a time series for each sensor).

Once sources are estimated, they are not any more a difference of potential, they are brain activity, or electrical currents (actually current densities) in the brain, they can be [Ampere*m]. These signals are in the source space, thus, there is one time series per vertex in your source space if sources are constrained. Otherwise, If you are working with unconstrained sources there are three time series per vertex). See this post for details on why [Ampere*m] are the units for current density:

Doubt about current density units: pA.m or pA/m2?

Hi Jose, Short answer: Brainstorm currently presents each grid point as a current dipole, in units of A-m. We may infer [B]surface[/B] density by dividing the dipole strength by the implied area around each dipole, and we may infer [B]volume[/B] density by additionally dividing out the implied cortical thickness. So if we run the Brainstorm Median Nerve Sample as a minimum norm, for 15,000 vertices, we see peaks of about 500 pA-m around 35ms post-stimulus. If we reducepatch the cortex to 1,500…

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@YYingH1213

The original signal is expressed in amplitude, and the source reconstruction is expressed in current density. In this case, there will be a problem, because we are performing synchronous coupling analysis on multimodal EEG and EMG signals, and EMG signals are expressed in amplitude.

This should not a problem, the connectivity metric will compute between time series, regardless of their units. Moreover, most of connectivity metrics are not affected by the scale of the signals. You can check this tutorial where coherence between EMG and sources is computed:

https://neuroimage.usc.edu/brainstorm/Tutorials/CorticomuscularCoherence