Estimates brain source activity from scalp EEG data using distributed
source imaging methods. Requires a forward model from
eegForwardModel.
Usage
eegSourceEstimate(
x,
forward_model,
method = c("sloreta", "eloreta", "mne", "dspm"),
lambda = 0.05,
assay_name = NULL,
output_assay = "source"
)Arguments
- x
A PhysioExperiment object with EEG data.
- forward_model
A forward model list as returned by
eegForwardModel.- method
Source estimation method:
"sloreta"(standardized low-resolution tomography),"eloreta"(exact low-resolution tomography),"mne"(minimum norm estimate), or"dspm"(dynamic statistical parametric mapping; MNE noise-normalized by each source's projected noise sensitivity, Dale et al. 2000).- lambda
Regularization parameter (default: 0.05). Higher values produce smoother solutions.
- assay_name
Name of the input assay. If
NULL, the default assay is used.- output_assay
Name for the output assay containing source estimates (default:
"source"). The metadata names"source_estimate","beamformer_info", and"source_plot_default"are reserved.
Value
Modified PhysioExperiment with source estimates stored in
output_assay. The assay is a matrix of dimensions
n_time x (n_sources * 3). Sets metadata(x)$source_estimate
with a list containing: method, lambda,
n_sources, n_source_cols, source_positions,
output_assay, orientation_count, and coordinate provenance.
References
Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA). Methods and Findings in Experimental and Clinical Pharmacology, 24(Suppl D), 5-12.
Examples
if (FALSE) { # \dontrun{
pe <- make_eeg(n_time = 500, n_channels = 19, sr = 250)
fm <- eegForwardModel(pe, method = "spherical", n_sources = 50)
pe <- eegSourceEstimate(pe, fm, method = "mne")
} # }