PyZapline_plus

MNE Integration

PyZaplinePlus provides an optional adapter to clean mne.io.Raw objects directly, preserving channel metadata and annotations.

Basic usage

import mne
from pyzaplineplus import apply_zapline_to_raw

raw = mne.io.read_raw_fif("my_raw.fif", preload=True)
raw_clean, config, analytics, figs = apply_zapline_to_raw(
    raw,
    picks=None,           # default: EEG-only
    copy=True,            # return a modified copy
    line_freqs="line",   # auto-detect 50/60 Hz
    plotResults=False,    # optional plotting
    adaptiveNremove=True, # MATLAB parity default
)

# Save if desired
raw_clean.save("my_raw_cleaned.fif", overwrite=True)

Key options

• picks: list of channel names to process. Defaults to EEG-only (mne.pick_types(eeg=True, ...)).
• copy: True to return a modified copy; False to operate in-place.
• line_freqs: sequence of frequencies (e.g. [50, 100]), “line” for 50/60 autodetect, or None to rely on automatic detection within bounds.
• dtype: the adapter preserves the underlying Raw dtype (e.g. float32) when writing back.

Troubleshooting

• preload: raw must be preload=True.
• channel types: default picks=None processes EEG channels only. Pass explicit names to include other types.
• events/annotations: these are preserved; only the selected channels’ sample data are modified.
• no mne installed: install extra dependencies with pip install pyzaplineplus[mne].

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