Re-reference EEG data

Applies a re-referencing scheme to EEG data. Re-referencing transforms the data by subtracting a reference signal from all channels, which can improve spatial resolution and comparability across studies.

Usage

eegRereference(
  x,
  ref_type = c("average", "robust", "median", "mastoids", "cz", "rest", "channel"),
  ref_channels = NULL,
  exclude = NULL,
  assay_name = NULL,
  output_assay = "rereferenced",
  robust_noise_sd = 4,
  robust_max_iter = 5
)

Arguments

x

A PhysioExperiment object.

ref_type

Re-referencing scheme: "average" (common average), "robust" (PREP robust average reference: iteratively detect bad channels and exclude them from the average, Bigdely-Shamlo et al., 2015), "median" (channel-wise median reference, robust to outlier channels), "mastoids" (linked mastoids), "cz" (Cz reference), "rest" (Reference Electrode Standardization Technique), or "channel" (user-specified channels).

ref_channels

Character vector of channel labels to use as reference (required for ref_type = "channel").

exclude

Character vector of channel labels to exclude from the average reference calculation (only used for ref_type = "average").

assay_name

Name of the assay to re-reference. If NULL, uses defaultAssay(x).

output_assay

Name of the output assay (default: "rereferenced").

robust_noise_sd

Noise threshold (SDs above the median channel variance) for bad-channel detection in ref_type = "robust" (default: 4).

robust_max_iter

Maximum PREP iterations for ref_type = "robust" (default: 5).

Value

A PhysioExperiment object with re-referenced data in the specified output assay. Stores reference info (including the channels excluded by the robust reference) in metadata(x)$reference and logs the step in the object's provenance.

References

Bigdely-Shamlo, N., et al. (2015). "The PREP pipeline: standardized preprocessing for large-scale EEG analysis." Frontiers in Neuroinformatics, 9, 16. doi:10.3389/fninf.2015.00016

Examples

if (FALSE) { # \dontrun{
pe <- make_eeg(n_time = 5000, n_channels = 19, sr = 500)
pe_avg    <- eegRereference(pe, ref_type = "average")
pe_robust <- eegRereference(pe, ref_type = "robust")
pe_median <- eegRereference(pe, ref_type = "median")
} # }