Prepare Gazepoint EDA data for CTSI sparse deconvolution workflows — prepare_gazepoint_ctsi

Prepares signal, event, and configuration tables for downstream continuous-time system identification (CTSI) sparse EDA deconvolution workflows. This function does not implement the full Amin-Faghih CTSI solver in R. It creates reproducible input objects and optional CSV files for external CTSI implementations.

Usage

prepare_gazepoint_ctsi_input(
  dat,
  eda_col = "GSR_US",
  time_col = "CNT",
  group_cols = NULL,
  event_onset_col = NULL,
  event_name_col = NULL,
  sampling_rate = NULL,
  tau0_range = c(2, 4),
  tau1_range = c(0.5, 1),
  sparsity_grid = c(0.001, 0.01, 0.1, 1),
  output_dir = NULL,
  prefix = "gazepoint_ctsi"
)

Arguments

dat: A data frame containing EDA data.
eda_col: Numeric EDA/conductance column.
time_col: Numeric time column.
group_cols: Optional grouping columns, such as participant/session.
event_onset_col: Optional event onset column.
event_name_col: Optional event/condition column.
sampling_rate: Optional sampling rate in Hz.
tau0_range: Candidate slow time-constant range.
tau1_range: Candidate fast time-constant range.
sparsity_grid: Candidate sparsity penalties.
output_dir: Optional directory for CSV export.
prefix: Output file prefix.

Value

A list with overview, signal_table, event_table, ctsi_config, ctsi_notes, written_files, and settings.