moabb.pipelines.classification.SSVEP_CCA#

class moabb.pipelines.classification.SSVEP_CCA(interval, freqs, n_harmonics=3)[source]#

Classifier based on Canonical Correlation Analysis for SSVEP.

A CCA is computed from the set of training signals and some pure sinusoids to act as reference. Classification is made by taking the frequency with the max correlation, as proposed in [1].

Parameters

  • interval (list of length 2) – List of form [tmin, tmax]. With tmin and tmax as defined in the SSVEP paradigm moabb.paradigms.SSVEP()

  • freqs (dict with n_classes keys) – Frequencies corresponding to the SSVEP stimulation frequencies. They are used to identify SSVEP classes presents in the data.

  • n_harmonics (int) – Number of stimulation frequency’s harmonics to be used in the generation of the CCA reference signal.

References

1

Bin, G., Gao, X., Yan, Z., Hong, B., & Gao, S. (2009). An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. Journal of neural engineering, 6(4), 046002. https://doi.org/10.1088/1741-2560/6/4/046002

fit(X, y, sample_weight=None)[source]#

Compute reference sinusoid signal.

These sinusoid are generated for each frequency in the dataset

predict(X)[source]#

Predict is made by taking the maximum correlation coefficient.

predict_proba(X)[source]#

Probability could be computed from the correlation coefficient.

set_fit_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → SSVEP_CCA[source]#

Request metadata passed to the fit 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 fit 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 fit.

  • 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.

New 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 fit.

Returns

self – The updated object.

Return type

object

set_score_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → SSVEP_CCA[source]#

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.

New 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