mne_denoise.asr.select_juggler_reference_samples#
- mne_denoise.asr.select_juggler_reference_samples(X: ndarray, sfreq: float, strategy: str = 'dbscan', selection_filter_kind: str = 'asr', dbscan_top_k: int = 5, dbscan_eps: float | str = 'auto', dbscan_min_samples: int | float | str = 'auto', gev_grid_size: int = 2048, min_reference_fraction: float = 0.05, verbose: bool | str | int | None = None) tuple[ndarray, ndarray, dict[str, Any]][source]#
Select calibration samples with JugglerASR rules.
- Parameters:
- Xndarray, shape (n_channels, n_times)
Continuous candidate calibration data.
- sfreqfloat
Sampling frequency in Hz.
- strategy{“dbscan”, “gev”}, default=”dbscan”
Reference-sample selection strategy.
- selection_filter_kind{“asr”, “highpass”, “none”}, default=”asr”
Statistics/pre-emphasis filter applied before selection.
- dbscan_top_kint, default=5
Number of largest channel amplitudes used as DBSCAN features.
- dbscan_epsfloat or {“auto”, “paper”}, default=”auto”
DBSCAN neighborhood radius.
- dbscan_min_samplesint, float, or {“auto”, “paper”}, default=”auto”
DBSCAN core-neighborhood count.
- gev_grid_sizeint, default=2048
Number of grid points used for GEV mode estimation.
- min_reference_fractionfloat, default=0.05
Minimum retained sample fraction.
- verbosebool, str, int, or None, default=None
Logging level.
- Returns:
- X_refndarray, shape (n_channels, n_selected_times)
Selected samples after the statistics/pre-emphasis filter.
- sample_maskndarray of bool, shape (n_times,)
Retained reference-sample mask.
- diagnosticsdict
Selection parameters, labels or GEV diagnostics, and retained counts [1].
References