EZFragility (version 1.0.3)

calcAdjFrag: Calculate adjacency matrices and fragility matrix from iEEG recording

Description

The function calculates the neural fragility column from an adjacency matrix in each time window

Usage

calcAdjFrag(
  epoch,
  window,
  step,
  lambda = NULL,
  nSearch = 100L,
  progress = FALSE,
  parallel = FALSE
)

Value

A Fragility object

Arguments

epoch

Matrix or Epoch object. iEEG data matrix or Epoch object. If matrix, the row names are the electrode names and the column names are the time points

window

Integer. The number of time points to use in each window

step

Integer. The number of time points to move the window each time

lambda

Numeric. The lambda value for regularization to use in the ridge regression. If NULL, the lambda will be chosen automatically ensuring that ensuring that the adjacent matrix is stable (see details)

nSearch

Integer. Number of instable eigenvalues with norm=1 to search for the minimum norm perturbation. This parameter is used only when the lambda is NULL

progress

Logical. If TRUE, print progress information. If parallel is TRUE, this option only support the doSNOW backend.

parallel

Logical. If TRUE, use parallel computing. Users must register a parallel backend with the foreach package

Details

1/ For each time window i, a discrete stable Linear time system (adjacency matrix) is computed named \(A_i\) such that \(A_i x(t) = x(t+1)\). The 'lambda' option is the regularization parameter for the ridge regression. lambda=NULL(default) will find a lambda value that ensures the stability of the estimated \(A_i\).

2/For each stable estimated \(A_i\), the minimum norm perturbation \(\Gamma_{ik}\) (k index of the electrodes) for column perturbation is computed. Each column is normalized \(\frac{max(\Gamma_{i})-\Gamma_{ik}}{max(\Gamma_i)}\)

Examples

## A dummy example with 5 electrodes and 20 time points
data <- matrix(rnorm(100), nrow = 5)
## create an Epoch object
epoch <- Epoch(data)
windowNum <- 10
step <- 5
lambda <- 0.1
calcAdjFrag(
    epoch = epoch, window = windowNum,
    step = step, lambda = lambda, progress = TRUE
)

## A more realistic example with parallel computing
# \donttest{
if (requireNamespace("doSNOW")) {
    ## Register a SNOW backend with 4 workers
    library(parallel)
    library(doSNOW)
    cl <- makeCluster(4, type = "SOCK")
    registerDoSNOW(cl)

    data("pt01EcoG")
    epoch <- Epoch(pt01EcoG)
    window <- 250
    step <- 125
    title <- "PT01 seizure 1"
    calcAdjFrag(
        epoch = epoch, window = window,
        step = step, parallel = TRUE, progress = TRUE
    )

    ## stop the parallel backend
    stopCluster(cl)
}
# }