pyavs.preprocess_meg_block¶
- pyavs.preprocess_meg_block(raw: mne.io.Raw, subject_id: int, session: int, block: int, apply_maxwell: bool = True, apply_filtering: bool = False, apply_resampling: bool = True, interpolate_bads: bool = True, l_freq: float = 0.2, h_freq: float = 200.0, resample_freq: float = 500.0, causal_filter: bool = False, bad_channels_file: str | None = None, crosstalk_file: str | None = None, fine_cal_file: str | None = None, verbose: bool = True) mne.io.Raw[source]¶
Complete preprocessing pipeline for a single MEG block.
- Parameters:
raw (mne.io.Raw) – Raw MEG data
subject_id (int) – Subject ID
session (int) – Session number
block (int) – Block number
apply_maxwell (bool, optional) – Whether to apply Maxwell filtering (default: True)
apply_filtering (bool, optional) – Whether to apply bandpass filtering (default: False)
apply_resampling (bool, optional) – Whether to resample data (default: True)
interpolate_bads (bool, optional) – Whether to interpolate bad channels (default: True)
l_freq (float, optional) – Low-pass frequency in Hz (default: 0.2)
h_freq (float, optional) – High-pass frequency in Hz (default: 100.0)
resample_freq (float, optional) – Resampling frequency in Hz (default: 500.0)
causal_filter (bool, optional) – Whether to apply causal filtering (default: False) If True, uses minimum-phase filtering which preserves temporal order
bad_channels_file (str, optional) – Path to bad channels file
crosstalk_file (str, optional) – Path to crosstalk file
fine_cal_file (str, optional) – Path to fine calibration file
verbose (bool, optional) – Whether to print progress information (default: True)
- Returns:
Preprocessed raw data
- Return type:
Notes
By default, this function applies Maxwell filtering, bad channel interpolation, and resampling, but NOT bandpass filtering. Filtering should be applied later using the AVS composer filter_meg_data() method to allow for flexible analysis-specific filter parameters.