moabb.pipelines.classification.SSVEP_CCA

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

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

Methods

fit(X, y[, sample_weight])

Compute reference sinusoid signal

get_params([deep])

Get parameters for this estimator.

predict(X)

Predict is made by taking the maximum correlation coefficient

predict_proba(X)

Probabilty could be computed from the correlation coefficient

score(X, y[, sample_weight])

Return the mean accuracy on the given test data and labels.

set_params(**params)

Set the parameters of this estimator.

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

Compute reference sinusoid signal

These sinusoid are generated for each frequency in the dataset

predict(X)[source][source]

Predict is made by taking the maximum correlation coefficient

predict_proba(X)[source][source]

Probabilty could be computed from the correlation coefficient