moabb.pipelines.classification.SSVEP_itCCA#

class moabb.pipelines.classification.SSVEP_itCCA[source]#

Individual Template CCA (itCCA) for SSVEP detection [1].

itCCA replaces the sinusoidal reference signals used in standard CCA with individual templates computed by averaging training trials for each stimulus frequency. This captures subject-specific SSVEP morphology that sinusoidal references cannot model.

Mathematical Formulation

For each stimulus frequency \(f_n\), the individual template is computed as the average across all \(N_t\) training trials:

\[\bar{\mathbf{X}}_n = \frac{1}{N_t} \sum_{h=1}^{N_t} \mathbf{X}_n^{(h)}\]

For a test signal \(\mathbf{X}\), CCA is computed between the test data and each individual template \(\bar{\mathbf{X}}_n\):

\[\rho_n = \max_{\mathbf{w}_x, \mathbf{w}_y} \text{corr}(\mathbf{X}^T \mathbf{w}_x, \bar{\mathbf{X}}_n^T \mathbf{w}_y)\]

Classification Rule

\[\hat{f} = \arg\max_{n} \rho_n\]
classes_#

Class labels in the same label space as y.

Type:

ndarray of shape (n_classes,)

freqs_#

List of stimulus frequencies from training data.

Type:

list of str

one_hot_#

Mapping from class labels to class indices.

Type:

dict

le_#

Fitted label encoder.

Type:

LabelEncoder

templates_#

Dictionary mapping frequency strings to averaged templates of shape (n_channels, n_times).

Type:

dict

References

[1]

Nakanishi, M., Wang, Y., Wang, Y.-T., Mitsukura, Y., & Jung, T.-P. (2014). A high-speed brain speller using steady-state visual evoked potentials. International Journal of Neural Systems, 24(06), 1450019. https://doi.org/10.1142/S0129065714500191

Notes

Added in version 1.2.0.

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

Compute individual templates by averaging trials per class.

Parameters:

  • X (MNE Epochs) – Training data as MNE Epochs object.

  • y (ndarray of shape (n_trials,)) – Label vector with frequency strings for each trial.

  • sample_weight (Unused) – Only for compatibility with scikit-learn.

Returns:

self – Fitted instance.

Return type:

SSVEP_itCCA

predict(X)[source]#

Predict class labels for test data.

Parameters:

X (MNE Epochs) – Test data as MNE Epochs object.

Returns:

y – Predicted labels.

Return type:

list of int

predict_proba(X)[source]#

Predict class probabilities from correlation coefficients.

Parameters:

X (MNE Epochs) – Test data as MNE Epochs object.

Returns:

P – Probability of each class for each trial.

Return type:

ndarray of shape (n_trials, n_classes)

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

Configure whether metadata should be requested to be passed to the fit 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 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.

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 fit.

Returns:

self – The updated object.

Return type:

object

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

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