Applies spatial filtering (beamforming) to localize neural source power. Linearly Constrained Minimum Variance (LCMV) beamformer operates in the time domain. Dynamic Imaging of Coherent Sources (DICS) operates in the frequency domain.
Usage
eegBeamformer(
x,
forward_model,
method = c("lcmv", "dics"),
freq_range = NULL,
assay_name = NULL,
output_assay = "beamformer"
)Arguments
- x
A PhysioExperiment object with EEG data.
- forward_model
A forward model list as returned by
eegForwardModel.- method
Beamformer method:
"lcmv"(Linearly Constrained Minimum Variance) or"dics"(Dynamic Imaging of Coherent Sources).- freq_range
Numeric vector of length 2 specifying frequency range in Hz for DICS method (e.g.,
c(8, 13)for alpha band). Ignored for LCMV.- assay_name
Name of the input assay. If
NULL, the default assay is used.- output_assay
Name for the output assay containing beamformer results (default:
"beamformer"). The metadata names"source_estimate","beamformer_info", and"source_plot_default"are reserved.
Value
Modified PhysioExperiment with source power stored in
output_assay as a matrix with one column named "power"
(n_sources rows). Each value represents the estimated source power
at the corresponding dipole location. Plotting metadata, including source
positions, the output name, method, and coordinate provenance, is stored
in metadata(x)$beamformer_info.
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.
Van Veen, B. D., et al. (1997). Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Transactions on Biomedical Engineering, 44(9), 867-880.
Examples
if (FALSE) { # \dontrun{
pe <- make_eeg(n_time = 1000, n_channels = 19, sr = 250)
fm <- eegForwardModel(pe, method = "spherical", n_sources = 50)
pe <- eegBeamformer(pe, fm, method = "lcmv")
} # }