moabb.pipelines.classification.SSVEP_MsetCCA#
- class moabb.pipelines.classification.SSVEP_MsetCCA(freqs, n_filters=1, n_jobs=1)[source]#
Classifier based on MsetCCA for SSVEP.
The MsetCCA method learns multiple linear transforms to extract SSVEP common features from multiple sets of EEG data. These are then used to compute the reference signal used in CCA [1].
- Parameters
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_filters (int) – Number of multisets spatial filters used per sample data. It corresponds to the number of eigen vectors taken the solution of the MAXVAR objective function as formulated in Eq.5 in [1].
References
- 1(1,2)
Zhang, Y.U., Zhou, G., Jin, J., Wang, X. and Cichocki, A. (2014). Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis. International journal of neural systems, 24(04), p.1450013. https://doi.org/10.1142/S0129065714500130
Notes
New in version 0.5.0.
- fit(X, y, sample_weight=None)[source]#
Compute the optimized reference signal at each stimulus frequency.
- set_fit_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') SSVEP_MsetCCA [source]#
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.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.
- set_score_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') SSVEP_MsetCCA [source]#
Request metadata passed to the
score
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.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.