pyavs.find_eye_components_xy_correlation

pyavs.find_eye_components_xy_correlation(ica: mne.preprocessing.ICA, meg_raw: mne.io.Raw, et_gaze_epochs: mne.EpochsArray, top_fraction: float = 0.05, reject: dict | None = None, verbose: bool = True) → Tuple[List[int], DataFrame][source]

Find ICA components correlated with per-scene XY gaze position.

IC sources are epoched with the same scene_on events as et_gaze_epochs and then both are flattened across epochs before computing Pearson r. The top top_fraction of components ranked by max(abs(r_gx), abs(r_gy)) are flagged as eye components.

Parameters:

  • ica (mne.preprocessing.ICA) – Fitted ICA object.

  • meg_raw (mne.io.Raw) – MEG raw data (unfiltered; used to compute ICA source epochs).

  • et_gaze_epochs (mne.EpochsArray) – Per-scene gaze epochs from build_et_gaze_epochs_per_scene(), with ‘gx’ and ‘gy’ channels. Its .events and .tmin / .tmax drive the matching MEG epoching.

  • top_fraction (float, optional) – Fraction of components to flag as eye-related, ranked by max_r (default: 0.05 → top 5 %).

  • reject (dict or None, optional) – Amplitude rejection thresholds applied when creating MEG epochs (e.g. dict(grad=4000e-13, mag=4e-12)). ET epochs are synced to the surviving MEG epochs after dropping. None keeps all epochs.

  • verbose (bool, optional) – Whether to log results.

Returns:

(eye_component_indices, scores_df) where scores_df has columns ‘component’, ‘r_gx’, ‘r_gy’, ‘max_r’.

Return type:

tuple