msqms.qc.entropy_metrics module

Entropy Domain Metric for MEG Data.

Summary

Classes:

EntropyDomainMetric

Class to calculate entropy domain metrics for MEG data.

Reference

class msqms.qc.entropy_metrics.EntropyDomainMetric(raw, data_type, origin_raw, n_jobs=-1, verbose=False)

Bases: Metrics

Class to calculate entropy domain metrics for MEG data.

This class processes segmented MEG data and computes entropy-related metrics for each segment.

Parameters:

• raw (mne.io.Raw) – The raw MEG data.

• data_type (str) – The type of MEG data (e.g., ‘opm’ or ‘squid’).

• origin_raw (mne.io.Raw) – The original raw MEG data for comparison.

• n_jobs (int, optional) – Number of parallel jobs to use for computation. Default is -1 (use all available cores).

• verbose (bool, optional) – If True, enables verbose output. Default is False.

compute_metrics(meg_type, seg_length=100)

Compute entropy domain metrics for segmented MEG data.

Parameters:

• meg_type (MEG_TYPE) – Type of MEG channels to process (e.g., ‘mag’, ‘grad’).

• seg_length (int, optional) – Length of each segment for computation, in seconds. Default is 100.

Returns:

meg_metrics_df – DataFrame containing the averaged entropy metrics for the MEG data.

Return type:

pd.DataFrame

compute_entropies(data)

Calculate entropy-related features for all channels.

Parameters:

data (np.ndarray) – Multichannel time series data.

Returns:

entropy_df – DataFrame containing entropy-related metrics for all channels.

Return type:

pd.DataFrame

compute_energy_entropy(data)

Compute energy and energy entropy for all channels.

Parameters:

data (np.ndarray) – Multichannel time series data.

Returns:

energy_entropy_df – DataFrame containing energy and energy entropy metrics for all channels.

Return type:

pd.DataFrame