Interpret EMG Network with Knowledge-Graph Metadata

Adds metadata/KG context to high-weight network edges. This function does not require a specific backend and works with user-provided tables exported from PhysioAnnotationHub/physioKG workflows.

Usage

emgInterpretNetworkKG(
  network,
  node_metadata = NULL,
  kg_edges = NULL,
  threshold = NULL,
  top_n = 20L,
  window = NULL
)

Arguments

network

A square matrix (channels x channels) or 3D array (window x channels x channels).

node_metadata

Optional data.frame containing at least channel. Additional columns (e.g. kg_node, muscle_name, muscle_group) are carried into the edge table.

kg_edges

Optional data.frame of KG links. Expected columns are node_a, node_b, and optional relation.

threshold

Optional edge threshold. Default is 75th percentile of upper-triangle weights.

top_n

Maximum number of edges returned after thresholding.

window

Optional window index when network is a 3D array. If NULL, uses the mean across windows.

Value

A list with:

edge_table

Ranked edge table with optional metadata/KG annotations.

threshold

Applied threshold value.

network_matrix

Matrix used for interpretation.

summary

List of summary statistics.

kg_relation_summary

Relation counts from matched KG links, or NULL.

Examples

pe <- make_emg(n_time = 2000, n_channels = 4, sr = 1000)
net <- emgCoherenceNetwork(pe, freq_band = c(20, 150))
interp <- emgInterpretNetworkKG(net$network, top_n = 5)
interp$edge_table
#>   source target    weight
#> 1   EMG1   EMG3 0.1888128
#> 2   EMG1   EMG4 0.1815934