gedai.gedai.Gedai🔗

class gedai.gedai.Gedai[source]🔗

Generalized Eigenvalue De-Artifacting Instrument (GEDAI).

This class implements the single band GEDAI workflow. For wavelet-based decomposition, band-wise denoising use MultibandGedai.

See [1].

Warning

For EEG channels, Gedai will set average reference internally to match the leadfield covariance reference. Gedai will not modify the input data in-place, but will create copies when necessary to ensure the original data remains unchanged.

Attributes

ch_names

Get the channel names used during fitting.

Methods

`fit_epochs`(epochs[, picks, reference_cov, ...])	Fit the GEDAI model to the epochs data.
`fit_raw`(raw[, picks, duration, overlap, ...])	Fit the GEDAI model to the raw data.
`plot_fit`()	Plot the fitting results.
`transform_epochs`(epochs[, n_jobs, verbose])	Transform epochs data using the fitted model.
`transform_raw`(raw[, overlap, n_jobs, verbose])	Transform raw data using the fitted model.

References

fit_epochs(epochs, picks='eeg', reference_cov='leadfield', sensai_method='optimize', noise_multiplier=3.0, n_jobs=None, verbose=None)[source]🔗

Fit the GEDAI model to the epochs data.

Parameters:

epochsmne.Epochs: The epochs data to fit the model to.
picksstr | list | slice: Channels to include. Note that all channels selected must have the same type. Slices and lists of integers will be interpreted as channel indices. In lists, channel name strings (e.g. ['Fp1', 'Fp2']) will pick the given channels. Can also be the string values "all" to pick all channels, or "data" to pick data channels. The default is "eeg" to pick all EEG channels.
reference_covstr | mne.Covariance: The reference covariance to use. If 'leadfield', use a pre-computed covariance. The precomputed covariance if computed from a leadfield made using 1005 EEG channels layout and fsaverage head model. If mne.Covariance, use a pre-computed covariance. See compute_covariance_from_forward() for more details on how to compute a covariance from a forward solution.
sensai_methodstr: The method to use for threshold optimization. Can be 'gridsearch' or 'optimize'. The default is 'optimize'.
noise_multiplierfloat: The noise multiplier to use for artefact threshold rejection optimization.
n_jobsint | None: The number of jobs to run in parallel. If -1, use all available CPU cores. If 1 or None, computations are run serially.
verboseint | str | bool | None: Sets the verbosity level. The verbosity increases gradually between "CRITICAL", "ERROR", "WARNING", "INFO" and "DEBUG". If None is provided, the verbosity is set to "WARNING". If a bool is provided, the verbosity is set to "WARNING" for False and to "INFO" for True.

fit_raw(raw, picks='eeg', duration=1.0, overlap=0.75, reject_by_annotation=False, reference_cov='leadfield', sensai_method='optimize', noise_multiplier=3.0, n_jobs=None, verbose=None)[source]🔗

Fit the GEDAI model to the raw data.

Parameters:

rawmne.io.BaseRaw: The raw data to fit the model to.
picksstr | list | slice: Channels to include. Note that all channels selected must have the same type. Slices and lists of integers will be interpreted as channel indices. In lists, channel name strings (e.g. ['Fp1', 'Fp2']) will pick the given channels. Can also be the string values "all" to pick all channels, or "data" to pick data channels. The default is "eeg" to pick all EEG channels.
durationfloat: Duration of each epoch in seconds. The default is 1.0.
overlapfloat: The overlap ratio between consecutive epochs, between 0 and 1. The default is 0.5 (50%% overlap). For example, 0.5 means 50%% overlap and 0.75 means 75%% overlap.
reject_by_annotationbool: Whether annotated bad segments should be rejected. It is recommended to set this to False for fitting, since the algorithm needs to learn from bad segments. The default is False.
reference_covstr | mne.Covariance: The reference covariance to use. If 'leadfield', use a pre-computed covariance. The precomputed covariance if computed from a leadfield made using 1005 EEG channels layout and fsaverage head model. If mne.Covariance, use a pre-computed covariance. See compute_covariance_from_forward() for more details on how to compute a covariance from a forward solution.
sensai_methodstr: The method to use for threshold optimization. Can be 'gridsearch' or 'optimize'. The default is 'optimize'.
noise_multiplierfloat: The noise multiplier to use for artefact threshold rejection optimization.
n_jobsint | None: The number of jobs to run in parallel. If -1, use all available CPU cores. If 1 or None, computations are run serially.
verboseint | str | bool | None: Sets the verbosity level. The verbosity increases gradually between "CRITICAL", "ERROR", "WARNING", "INFO" and "DEBUG". If None is provided, the verbosity is set to "WARNING". If a bool is provided, the verbosity is set to "WARNING" for False and to "INFO" for True.

plot_fit()[source]🔗

Plot the fitting results.

Returns:

figslist of matplotlib.figure.Figure: The list of figures showing the fitting results.

transform_epochs(epochs, n_jobs=None, verbose=None)[source]🔗

Transform epochs data using the fitted model.

Parameters:

epochsmne.Epochs: The epochs to transform.
n_jobsint | None: The number of jobs to run in parallel. If -1, use all available CPU cores. If 1 or None, computations are run serially.
verboseint | str | bool | None: Sets the verbosity level. The verbosity increases gradually between "CRITICAL", "ERROR", "WARNING", "INFO" and "DEBUG". If None is provided, the verbosity is set to "WARNING". If a bool is provided, the verbosity is set to "WARNING" for False and to "INFO" for True.

Returns:

epochsmne.Epochs: The transformed epochs.

transform_raw(raw, overlap=0.75, n_jobs=None, verbose=None)[source]🔗

Transform raw data using the fitted model.

Parameters:

rawmne.io.BaseRaw: The raw data to fit the model to.
overlapfloat: The overlap ratio between consecutive epochs, between 0 and 1. The default is 0.5 (50%% overlap). For example, 0.5 means 50%% overlap and 0.75 means 75%% overlap.
n_jobsint | None: The number of jobs to run in parallel. If -1, use all available CPU cores. If 1 or None, computations are run serially.
verboseint | str | bool | None: Sets the verbosity level. The verbosity increases gradually between "CRITICAL", "ERROR", "WARNING", "INFO" and "DEBUG". If None is provided, the verbosity is set to "WARNING". If a bool is provided, the verbosity is set to "WARNING" for False and to "INFO" for True.

Returns:

raw_correctedmne.io.BaseRaw: The corrected raw data.

property ch_names🔗: Get the channel names used during fitting.