eegdash.features.feature_bank.signal module

class eegdash.features.feature_bank.signal.HilbertFeatureExtractor(feature_extractors: Dict[str, Callable], **preprocess_kwargs: Dict)

Bases: FeatureExtractor

parent_extractor_type = (<class 'eegdash.features.extractors.FeatureExtractor'>,)

preprocess(x)

Apply pre-processing to the input data.

Parameters:

• *x (tuple) – Input data.

• **kwargs – Additional keyword arguments.

Returns:

The pre-processed data.

Return type:

tuple

eegdash.features.feature_bank.signal.signal_decorrelation_time(x, fs=1)

eegdash.features.feature_bank.signal.signal_hjorth_activity(x, **kwargs)

eegdash.features.feature_bank.signal.signal_hjorth_complexity(x)

eegdash.features.feature_bank.signal.signal_hjorth_mobility(x)

eegdash.features.feature_bank.signal.signal_kurtosis(x, **kwargs)

eegdash.features.feature_bank.signal.signal_line_length(x)

eegdash.features.feature_bank.signal.signal_mean(x)

eegdash.features.feature_bank.signal.signal_peak_to_peak(x, **kwargs)

eegdash.features.feature_bank.signal.signal_quantile(x, q: Number = 0.5, **kwargs)

eegdash.features.feature_bank.signal.signal_root_mean_square(x)

eegdash.features.feature_bank.signal.signal_skewness(x, **kwargs)

eegdash.features.feature_bank.signal.signal_std(x, **kwargs)

eegdash.features.feature_bank.signal.signal_variance(x, **kwargs)

eegdash.features.feature_bank.signal.signal_zero_crossings(x, threshold=1e-15)