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Circular EEG connectogram

Source: R/eeg-connectogram.R
eegPlotConnectogram.Rd

Draws a deterministic circular view of a connectivity matrix stored in metadata(x)$connectivity$matrix. Symmetric matrices are represented by one edge per unordered channel pair. Asymmetric matrices retain every ordered entry, following the PhysioEEG directed-matrix convention of rows as targets and columns as sources.

Usage

eegPlotConnectogram(
  x,
  order = c("hemisphere", "lobe", "cluster"),
  modules = NULL,
  threshold = NULL,
  bundle = TRUE
)

Arguments

x

A PhysioExperiment with a finite square numeric matrix at metadata(x)$connectivity$matrix.

order

Exactly one node ordering: "hemisphere", "lobe", or "cluster". Cluster ordering uses average-linkage clustering of symmetrized absolute connectivity and deterministic merge orientation.

modules

Optional module declaration. Supply a named character or factor vector, or a data frame with unique label and module columns covering every channel. If NULL, an exact colData(x)$module column is used when present, otherwise lobe.

threshold

A finite non-negative scalar, or NULL for zero.

bundle

One non-missing logical scalar. If TRUE, edges follow deterministic cubic Bezier paths through module hubs; otherwise they are straight.

Value

A ggplot2 object. Resolved node, edge, path, module-arc, and setting tables are available in attr(plot, "connectogram_data").

Details

The threshold is a strict absolute display threshold (abs(value) > threshold); it is not a p-value or a corrected significance mask. Hemisphere and lobe are inferred only for recognized 10-20/10-10 labels. Other labels remain "unknown" unless explicit colData columns are supplied. Label tie-breaking uses a stable UTF-8 byte key. Edge identifiers length-prefix both endpoint labels so labels containing the visible " -- " or " -> " separators cannot collide.

See also

eegPlotConnectivity(), eegConnectivityMatrix()

Examples

if (FALSE) { # \dontrun{
pe <- make_eeg(n_time = 1000, n_channels = 4, sr = 250)
mat <- matrix(c(
  1, 0.5, 0, -0.4,
  0.5, 1, 0.3, 0,
  0, 0.3, 1, 0.6,
  -0.4, 0, 0.6, 1
), 4, 4)
labels <- SummarizedExperiment::colData(pe)$label
dimnames(mat) <- list(labels, labels)
metadata(pe)$connectivity <- list(
  matrix = mat, method = "coherence", band = c(8, 13)
)
eegPlotConnectogram(pe, threshold = 0.2)
} # }