pyntbci.stopping.MarginStopping

class pyntbci.stopping.MarginStopping(estimator: ClassifierMixin, segment_time: float, fs: int, target_p: float = 0.95, margin_min: float = 0.0, margin_max: float = 1.0, margin_step: float = 0.05, max_time: float = None, min_time: float = None)[source]

Margin dynamic stopping. Learns threshold margins (difference between best and second-best score) to stop at such that a targeted accuracy is reached [3].

Parameters:

  • estimator (ClassifierMixin) – The classifier object that performs the classification.

  • segment_time (float) – The size of a segment of data at which classification is performed ins seconds.

  • fs (int) – The sampling frequency of the EEG data in Hz.

  • target_p (float (default: 0.95)) – The targeted probability of correct classification.

  • margin_min (float (default: 0.0)) – The minimum value for the possible threshold margin to stop at.

  • margin_max (float (default: 1.0)) – The maximum value for the possible threshold margin to stop at.

  • margin_step (float (default: 0.05)) – The step size defining the resolution of the threshold margins at which to stop.

  • max_time (float (default: None)) – The maximum time in seconds at which to force a stop, i.e., a classification. Trials will not be longer than this maximum time. If None, the algorithm will always emit -1 if it cannot stop.

  • min_time (float (default: None)) – The minimum time in seconds at which a stop is possible, i.e., a classification. Before the minimum time, the algorithm will always emit -1. If None, the algorithm allows a stop already after the first segment of data.

margins_

The trained stopping margins of shape (n_segments).

Type:

NDArray

References

fit(X: ndarray[tuple[int, ...], dtype[_ScalarType_co]], y: ndarray[tuple[int, ...], dtype[_ScalarType_co]]) → ClassifierMixin[source]

The training procedure to fit the dynamic procedure on supervised EEG data.

Parameters:

  • X (NDArray) – The matrix of EEG data of shape (n_trials, n_channels, n_samples).

  • y (NDArray) – The vector of ground-truth labels of the trials in X of shape (n_trials).

Returns:

self – Returns the instance itself.

Return type:

ClassifierMixin

predict(X: ndarray[tuple[int, ...], dtype[_ScalarType_co]]) → ndarray[tuple[int, ...], dtype[_ScalarType_co]][source]

The testing procedure to apply the estimator to novel EEG data using margin dynamic stopping.

Parameters:

X (NDArray) – The matrix of EEG data of shape (n_trials, n_channels, n_samples).

Returns:

y – The vector of predicted labels of the trials in X of shape (n_trials). Note, the value equals -1 if the trial cannot yet be stopped.

Return type:

NDArray

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → MarginStopping

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object