API Reference

Classifiers

rCCA(stimulus, fs[, event, onset_event, ...])	Reconvolution CCA classifier.
eCCA(lags, fs[, cycle_size, ...])	ERP CCA classifier.
eTRCA(lags, fs[, cycle_size, ...])	ERP TRCA classifier.
Ensemble(estimator, gate)	Ensemble classifier.

Envelope

envelope_gammatone(audio, fs[, fs_inter, ...])	Compute the envelope of audio using a gammatone filterbank.
envelope_rms(audio, fs[, fs_inter, fs_target])	Compute the envelope of the audio as the root mean square (RMS) of the signal.

Gates

AggregateGate([aggregate])	Gate described by an aggregate function.
DifferenceGate(estimator)	Gate described by classification of difference scores.

Plotting

eventplot(S, E, fs[, ax, upsample, plotfs, ...])	Plot the event time-series.
topoplot(z, locfile[, cbar, ax, iso, chan])	Plot a topoplot.

Stimulus

make_apa_sequence()	Make an almost perfect auto-correlation (APA) sequence.
make_de_bruijn_sequence([k, n, seed])	Make a de Bruijn sequence.
make_golay_sequence()	Make complementary Golay sequences.
make_gold_codes([poly1, poly2, seed1, seed2])	Make a set of Gold codes.
make_m_sequence([poly, base, seed])	Make a maximum length sequence.
is_de_bruijn_sequence(stimulus[, k, n])	Check whether a stimulus is a de Bruijn sequence.
is_gold_code(stimulus)	Check whether a stimulus is a Gold code.
is_m_sequence(stimulus)	Check whether a stimulus is an m-sequence.
modulate(stimulus)	Modulate a stimulus.

Stopping

BayesStopping(estimator, segment_time, fs[, ...])	Bayesian dynamic stopping.
BetaStopping(estimator[, target_p, fs, max_time])	Beta dynamic stopping.
CriterionStopping(estimator, segment_time, fs)	Criterion static stopping.
MarginStopping(estimator, segment_time, fs)	Margin dynamic stopping.

Transformers

CCA(n_components[, gamma_x, gamma_y, ...])	Canonical correlation analysis (CCA).
TRCA([n_components])	Task related component analysis (TRCA).

Utilities

correlation(A, B)	Compute the correlation coefficient.
euclidean(A, B)	Compute the Euclidean distance.
covariance(X[, n_old, avg_old, cov_old, ...])	Compute the covariance matrix.
decoding_matrix(data, length[, stride])	Make a Hankel-like decoding matrix.
encoding_matrix(stimulus, length[, stride, ...])	Make a Toeplitz-like encoding matrix.
event_matrix(stimulus, event[, onset_event])	Make an event matrix.
correct_latency(X, y, latency, fs[, axis])	Correct for a latency in data.
filterbank(X, passbands, fs[, tmin, ftype, ...])	Apply a filterbank.
trials_to_epochs(X, y, codes, epoch_size, ...)	Slice trials to epochs.
itr(n, p, t)	Compute the information-transfer rate (ITR).