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[Any, ...], dtype[_ScalarT]], y: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → 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[Any, ...], dtype[_ScalarT]]) → ndarray[tuple[Any, ...], dtype[_ScalarT]][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

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

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