LIT LAB iEEG preprocessing pipeline (WIP)

Preprocessing methodology in this repo is based on Regev and Casto et al. (2024).

• Regev, T.I., Casto, C., Hosseini, E.A. et al. Neural populations in the language network differ in the size of their temporal receptive windows. Nat Hum Behav 8, 1924–1942 (2024). https://doi.org/10.1038/s41562-024-01944-2

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Interictal (IED) pipeline

Detection of interictal epileptiform discharges from sEEG EDF files (Janca et al., 2015). The pipeline: loads raw EDF → labels L/R channels as sEEG → resamples → bandpass + notch → Hilbert envelope → lognormal threshold → spike detection → per-channel counts/rates and a boolean mask of high-rate channels.

Quick start:

# With a config file (copy batch_script/ied_config_example.json and set raw_path, output_dir)
python -m preprocessing.IED_analysis --config my_config.json

# Or with CLI args only
python -m preprocessing.IED_analysis --raw-path /path/to/file.edf --output-dir /path/to/output

Outputs (in output_dir): ied_results.npz (spike markers, channel counts/rates, channels_above_rate boolean, channel names), ied_metadata.json (summary + config). Optional intermediates with --return-intermediates.

Full documentation: docs/INTERICTAL_PIPELINE_STEPS.md — config format, all parameters, output descriptions, HPC/SLURM usage, and example Python code to load results.

Setup

pip install -r requirements.txt

Requires: mne, numpy, scipy, and optionally jaxtyping for shape annotations in the interictal module.