meeg-utils Documentation#
A Python-based MEG/EEG processing toolkit built on MNE-Python, providing a high-level, user-friendly API for preprocessing electrophysiological data.
Features#
High-level API - Simple, intuitive interface for complex preprocessing pipelines
BIDS Support - Native support for Brain Imaging Data Structure (BIDS)
Advanced Preprocessing:
Automated bad channel detection (PREP pipeline for EEG, Maxwell filtering for MEG)
Line noise removal (Zapline/Zapline-iter)
ICA-based artifact removal with automatic labeling (ICLabel for EEG, MEGNet for MEG)
Batch Processing - Parallel processing of multiple datasets
Type-safe - Full type hints for better IDE support
Well-tested - Comprehensive test suite with >80% coverage
Quick Start#
Installation#
pip install meeg-utils
Basic Usage#
from meeg_utils.preprocessing import PreprocessingPipeline
from mne_bids import BIDSPath
# Define input
bids_path = BIDSPath(
subject="01",
session="01",
task="rest",
datatype="eeg",
root="/path/to/bids/dataset"
)
# Create and run pipeline
pipeline = PreprocessingPipeline(
input_path=bids_path,
output_dir="/path/to/output"
)
raw_preprocessed = pipeline.run(
filter_params={"highpass": 0.1, "lowpass": 100.0, "sfreq": 250.0},
detect_bad_channels=True,
remove_line_noise=True,
apply_ica=True,
)
# Save results
pipeline.save()
API Reference
Developer Guide