meegkit.trca

Task-Related Component Analysis.

Functions

trca(X)	Task-related component analysis.
TRCA(sfreq, filterbank[, ensemble, method, ...])	Task-Related Component Analysis (TRCA).

class meegkit.trca.TRCA(sfreq, filterbank, ensemble=False, method='original', estimator='scm')

Bases: object

Task-Related Component Analysis (TRCA).

Parameters:

• sfreq (float) – Sampling rate.

• filterbank (list[[2-tuple, 2-tuple]]) –

  Filterbank frequencies. Each list element is itself a list of passband Wp and stopband Ws edges frequencies [Wp, Ws]. For example, this creates 3 bands, starting at 6, 14, and 22 hz respectively:

  [[(6, 90), (4, 100)],
   [(14, 90), (10, 100)],
   [(22, 90), (16, 100)]]
  

  See scipy.signal.cheb1ord() for more information on how to specify the Wp and Ws.

• ensemble (bool) – If True, perform the ensemble TRCA analysis (default=False).

• method (str in {'original'| 'riemann'}) – Use original implementation from [1] or a variation that uses regularization and the geodesic mean [2].

• regularization (str in {'schaefer' | 'lwf' | 'oas' | 'scm'}) – Regularization estimator used for covariance estimation with the riemann method. Consider ‘schaefer’, ‘lwf’, ‘oas’. ‘scm’ does not add regularization and is almost equivalent to the original implementation.

traindata

Reference (training) data decomposed into sub-band components by the filter bank analysis.

Type:

array, shape=(n_bands, n_chans, n_trials)

y_train

Labels associated with the train data.

Type:

array, shape=(n_trials)

coef_

Weight coefficients for electrodes which can be used as a spatial filter.

Type:

array, shape=(n_chans, n_chans)

classes

Classes.

Type:

list

n_bands

Number of sub-bands.

Type:

int

References

[1]

M. Nakanishi, Y. Wang, X. Chen, Y. -T. Wang, X. Gao, and T.-P. Jung, “Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis”, IEEE Trans. Biomed. Eng, 65(1):104-112, 2018.

[2]

Barachant, A., Bonnet, S., Congedo, M., & Jutten, C. (2010, October). Common spatial pattern revisited by Riemannian geometry. In 2010 IEEE International Workshop on Multimedia Signal Processing (pp. 472-476). IEEE.

__init__(sfreq, filterbank, ensemble=False, method='original', estimator='scm')

fit(X, y)

Training stage of the TRCA-based SSVEP detection.

Parameters:

• X (array, shape=(n_samples, n_chans[, n_trials])) – Training EEG data.

• y (array, shape=(trials,)) – True label corresponding to each trial of the data array.

predict(X)

Test phase of the TRCA-based SSVEP detection.

Parameters:

X (array, shape=(n_samples, n_chans[, n_trials])) – Test data.

Returns:

pred – The target estimated by the method.

Return type:

np.array, shape (trials)

meegkit.trca.trca(X)

Task-related component analysis.

This function implements the method described in [1].

Parameters:

X (array, shape=(n_samples, n_chans[, n_trials])) – Training data.

Returns:

W – Weight coefficients for electrodes which can be used as a spatial filter.

Return type:

array, shape=(n_chans,)

References

[1]

M. Nakanishi, Y. Wang, X. Chen, Y. -T. Wang, X. Gao, and T.-P. Jung, “Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis”, IEEE Trans. Biomed. Eng, 65(1):104-112, 2018.

meegkit.trca.trca_regul(X, method)

Task-related component analysis.

This function implements a variation of the method described in [1]. It is inspired by a riemannian geometry approach to CSP [2]. It adds regularization to the covariance matrices and uses the riemannian mean for the inter-trial covariance matrix S.

Parameters:

X (array, shape=(n_samples, n_chans[, n_trials])) – Training data.

Returns:

W – Weight coefficients for electrodes which can be used as a spatial filter.

Return type:

array, shape=(n_chans,)

References

[1]

M. Nakanishi, Y. Wang, X. Chen, Y. -T. Wang, X. Gao, and T.-P. Jung, “Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis”, IEEE Trans. Biomed. Eng, 65(1):104-112, 2018.

[2]

Barachant, A., Bonnet, S., Congedo, M., & Jutten, C. (2010, October). Common spatial pattern revisited by Riemannian geometry. In 2010 IEEE International Workshop on Multimedia Signal Processing (pp. 472-476). IEEE.