Source Localization (Beamformer)¶

This pipeline is very similar to the pipeline documented in Source Localization (sLORETA). However, this pipeline uses linearly constrained minimum variance (LCMV) beamforming for source estimation, rather than the sLORETA algorithm. The beamformer makes different assumptions from sLORETA, and can produce a more accurate signal reconstruction for source vertices of interest, especially in the presence of various noise sources, but the result may result in a less focal reconstruction of single-source activity under well-behaved conditions than what sLORETA or eLORETA can provide. We focus our discussion here only on the LCMV-related nodes, and refer the reader to the sLORETA pipeline walkthrough to learn about the other stages of the data flow, including data acquisition, pre-processing, source processing, and output.

The overall pipeline data flow is depicted in the following:

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The pathway in the uppermost row of nodes constitutes data acquisition and pre-processing, followed by source estimation, processing of source-space signals, and finally output in the lower two rows. The nodes relevant to source estimation are shown in the following cut-out:

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First, we perform common-average re-referencing, which is crucial when using the head models that are included with NeuroPype, since all of these assume that the data has been common-average referenced. The resulting EEG time series is then fed into the Data input port of the LCMV Beamforming node. The Beamforming node is somewhat untypical in that it has two input ports, called "Data" and "Covariance". The second input port, Covariance, accepts an estimate of the noise covariance that is used in the Beamforming algorithm. This covariance matrix typically changes over time, and is estimated from a few-second window of data, which is here accomplished using the Per-Element Covariance and Exponential Moving Average nodes. There are many alternative ways to estimate this covariance matrix, for instance using the Moving Average node instead of Exponential Moving Average, or by using special robust or regularized covariance estimators. When processing segmented (or epoched) EEG data, one can also use pre-stimulus EEG to estimate the covariance from, however, here, a sliding window is being used -- be sure to read the documentation of the moving-average node to understand what time range is effectively being used to estimate the covariance from.

The LCMV beamforming node has a number of parameters, which we briefly discuss in the following (for more information, be sure to read the node's documentation and parameter tooltips):

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The first parameter, the head model file is the type of head model to use. NeuroPype typically ships with a default head model based on the Colin27 brain, which is what is being used here. This file will work for any EEG data that has channels in the 10-20 labeling system. If you have other channel labels, you will need the locations for your channels and then either remap the channel labels to the closest 10-20 labels, or you can try to use the experimental interpolate option (see parameter documentation). The regularization strength is primarily used to prevent the covariance matrix from becoming degenerate, which can happen if the time window is too short, or there are too many channels, or some EEG sensors were electrically shorted. For these purposes, the default should be fine. We recommend leaving the Rescale activations setting at is defaults unless you need to squeeze out the last percent of computation speed from the node, and to leave the Normalize leadfield checkbox unchecked, unless you are dealing with a headmodel that has scaling issues (this can happen with some beta-quality head models, but does not apply to the default model).

The output of this node is a time series that has one channel for each brain source location in the head model, which, in the case of the NeuroPype default head model, correspond to the vertices of a mesh that conforms to the brain surface, resulting in ca. 4500 channels total.