pyntbci 1.9.0

Python Noise-Tagging Brain-Computer Interface (PyntBCI)

PyntBCI

The Python Noise-Tagging Brain-Computer Interfacing (PyntBCI) library is a specialized Python toolbox developed for the noise-tagging brain-computer interfacing (BCI) project at the Donders Institute for Brain, Cognition, and Behaviour at Radboud University in Nijmegen, the Netherlands. PyntBCI offers a suite of signal processing tools and machine learning algorithms tailored for BCIs using evoked responses, such as those recorded by electroencephalography (EEG). It is particularly focused on supporting code-modulated responses like the code-modulated visual evoked potential (c-VEP).

For detailed documentation as wel as tutorials and examples, see:

• https://thijor.github.io/pyntbci/

For a constructive review of the c-VEP BCI field, see:

• Martínez-Cagigal, V., Thielen, J., Santamaría-Vázquez, E., Pérez-Velasco, S., Desain, P., & Hornero, R. (2021). Brain–computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review. Journal of Neural Engineering. DOI: 10.1088/1741-2552/ac38cf

For an extensive literature overview, also see:

• https://github.com/thijor/cvep-literature.

For an example of an online BCI with PyntBCI, see our Dareplane implementation:

• https://github.com/thijor/dp-cvep
• https://github.com/thijor/dp-cvep-speller
• https://github.com/thijor/dp-cvep-decoder

Installation

To install PyntBCI, use:

pip install pyntbci

Getting started

Various tutorials and example analysis pipelines are provided in the tutorials/ and examples/ folder, which operate on synthetic EEG data generated on the fly (see pyntbci.eeg).

Referencing

When using PyntBCI, please reference at least one of the following:

• Thielen, J., van den Broek, P., Farquhar, J., & Desain, P. (2015). Broad-Band visually evoked potentials: re(con)volution in brain-computer interfacing. PLOS ONE, 10(7), e0133797. DOI: 10.1371/journal.pone.0133797
• Thielen, J., Marsman, P., Farquhar, J., & Desain, P. (2021). From full calibration to zero training for a code-modulated visual evoked potentials for brain–computer interface. Journal of Neural Engineering, 18(5), 056007. DOI: 10.1088/1741-2552/abecef

Contact

• Jordy Thielen ([e-mail])

Licensing

PyntBCI is licensed by the BSD 3-Clause License:

Copyright (c) 2021, Jordy Thielen All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Changelog

Version 1.9.0 (20-07-2026)

Added

• Added vmin and vmax to topoplot in plotting
• Added diff event to event_matrix in utilities
• Added eeg module to generate synthetic multi-channel EEG data (c-VEP signal, and noise)
• Added Vectorizer to transformers
• Added classes_ attribute to eCCA, Ensemble, rCCA in classifiers, AggregateGate, DifferenceGate in gates, and all classes in stopping, for scikit-learn ClassifierMixin compatibility
• Added a clear, actionable error message in CCA of transformers when a covariance matrix is singular or too ill-conditioned to invert (e.g. ensemble=True in eCCA/rCCA with too little data per class), instead of a bare "singular matrix" error or, worse, a silently corrupted result; documented this risk on ensemble in eCCA/rCCA
• Added a dtype parameter (default: "float32") to every generator function in eeg; all computation is still done internally in float64 (float64/complex128 for the FFT in generate_pink_noise) for numerical precision, and only cast to the requested dtype on the returned array
• Added inner and a running mode (with a *_old/n_old-style state, matching covariance's existing convention) to correlation and euclidean in utilities, letting a score matrix be updated with only newly observed samples instead of recomputed from scratch; correlation's running mode is implemented by reusing covariance's existing running mechanism directly (correlation is covariance normalized by the two variances, verified numerically identical), while euclidean/inner accumulate their own (simpler, since they need no running mean) raw sums
• Added running/reset parameters to decision_function/predict in eCCA/rCCA of classifiers (not ensemble=True), letting them be fed only the newly observed samples of a growing trial instead of recomputing the full spatial (and, for rCCA, spatio-spectral) filter and score from scratch every call; for rCCA, decoding_matrix's forward-looking window is handled by keeping a small raw-sample buffer and only committing a position's contribution once no future sample can still change it. All 5 stopping classes now use this internally in fit()'s calibration loop, and expose the same running/reset parameters on their own predict(); for a wrapped estimator that isn't an eCCA/rCCA (or has ensemble=True), a transparent fallback buffers the raw data and recomputes from scratch instead, so running=True still works (just without the speedup). Verified numerically identical to the non-running computation (max abs error ~1e-13, floating-point noise) across every score metric, n_components, and rCCA's decoding_length/decoding_stride combinations; measured 21x faster decision_function calls and 2x faster fit() calibration on an 8.4s trial (84 segments). This targets the predict-time per-trial scoring loop specifically; CCA in transformers already has a separate, pre-existing running mode for fit-time incremental covariance estimation across successive fit() calls (used to train the spatial filter itself) — the two are unrelated to each other by design, since predict-time running state (an in-progress trial's scores) and fit-time running state (the model's learned covariance) have different lifecycles, but both are built on the same underlying covariance() primitive
• Added examples/example_6_moabb.py, the first example to run on real (rather than synthetic) EEG data: the Thielen (2015) c-VEP dataset, loaded via MOABB and cross-validated with plain scikit-learn (StratifiedKFold + cross_val_score), since rCCA is itself a scikit-learn compatible estimator; needs the optional moabb/mne dependencies and downloads ~450 MB of data on first use, so it is excluded from execution when building the documentation (see filename_pattern in doc/conf.py)
• Added test coverage for Ensemble in classifiers (previously untested), the entire envelope module (previously had no test file), eventplot/stimplot in plotting (previously only topoplot was tested), find_neighbours, find_worst_neighbour, pinv, and trials_to_epochs in utilities (previously untested), and a new test_sklearn_compliance.py that checks get_params()/set_params()/clone() round-tripping across every classifier, gate, and stopping estimator in the library
• Added classification-correctness tests (accuracy = mean(yh == y) against a threshold, not just output shape) for eCCA, rCCA, Ensemble in classifiers, AggregateGate, DifferenceGate in gates, and all classes in stopping; previously every one of these tests only checked output shape, so a classifier that always predicted a constant class, or a decision_function computed backwards, would have still passed the whole suite
• Added a running parameter to eCCA/rCCA in classifiers, letting fit() be called repeatedly with new batches of trials that add to the previous fit instead of replacing it, by reusing CCA's own pre-existing running=True mechanism for the spatial (and, for rCCA, spatio-temporal) filter's covariance. For rCCA this is mathematically exact: its templates (Ts_/Tw_) are derived purely from the (fixed) stimulus and the current filter, never from the training trials, so fit(X1, y1) then fit(X2, y2) gives the identical filter (verified to ~1e-8) as one fit(concat(X1, X2), concat(y1, y2)) call. For eCCA it is necessarily an approximation, since its template is itself an average of the training trials and is used as the CCA fit's target on every call, so earlier calls see a less complete estimate of it than later ones; it converges towards the batch result as trials accumulate (verified: >0.999 cosine similarity to the batch filter after a few batches) but is not expected to equal it exactly, and is scoped to lags being set, template_metric="mean", and ensemble=False (the only combination with both a fixed, known-upfront class count and a single running template, rather than one needing to grow dynamically as new classes are observed or with no exact incremental form). Both raise a clear error on a channel/sample-count mismatch between calls, and both correctly restart a fresh running sequence (rather than silently resuming a stale one) if running is toggled off and back on between fit() calls. This targets fit-time incremental training, the counterpart to the predict-time running scoring added above; the two are unrelated by design but share the same covariance() primitive underneath

Changed

• Removed the bundled example/tutorial EEG data (data/) from the package; tutorials/ and examples/ now generate synthetic data with eeg instead
• Removed the pipelines/ folder of standalone analysis scripts for external published datasets
• Removed the mne dependency; examples/ now use Vectorizer in transformers instead of mne.decoding.Vectorizer
• Removed the 'seaborn' dependency; now simply relies on matplotlib
• Removed eTRCA from classifiers and TRCA from transformers
• Removed the estimator parameter from covariance in utilities (and, with it, estimator_x/estimator_y from CCA in transformers and cov_estimator_x/cov_estimator_t/cov_estimator_m from eCCA/rCCA in classifiers), along with the NotImplementedError it raised whenever a custom estimator was combined with running=True past the first call; the empirical covariance formula (the only implemented, tested path, since the custom-estimator path had no running-mode implementation to begin with) is used unconditionally instead
• Dropped Python 3.8 support (minimum is now 3.9), since the codebase already relied on builtin generic type hints (e.g. tuple[...]) that are not subscriptable at runtime on 3.8
• Migrated packaging metadata from the legacy setup.cfg to a PEP 621 [project] table in pyproject.toml (version and readme remain dynamic, sourced from pyntbci.__version__ and README.md/CHANGELOG.md respectively, as before); verified the built sdist/wheel metadata and bundled files are unchanged

Fixed

• Fixed BayesStopping in stopping check fitted if score-based
• Fixed itr in utilities output shape is input shape
• Fixed retaining dtype in stimulus
• Fixed DistributionStopping in stopping not checking fitted status when trained=False
• Fixed AggregateGate in gates modifying its aggregate parameter in __init__, which broke scikit-learn clone()
• Fixed rCCA in classifiers doing parameter resolution and computation in __init__ instead of fit, which left the estimator in a stale, inconsistent state after set_params()
• Fixed DistributionStopping in stopping validating distribution eagerly in __init__ instead of fit
• Fixed correlation in utilities, encoding_matrix in utilities (stimulus and length-1 length/stride lists), itr in utilities, get_T in eCCA of classifiers, and transform in CCA of transformers all mutating their input arguments in place as a side effect
• Fixed gamma_x/gamma_y regularization in CCA of transformers silently computing wrong (asymmetric, not positive semi-definite) covariance matrices whenever a per-feature array (rather than a single scalar) was used; scalar gamma_x/gamma_y behavior is unchanged
• Fixed examples/ and tests pre-computing an unseeded y externally and passing it into generate_c_vep of eeg, which bypassed eeg's own seeded label assignment and made random_state not actually reproduce the data; generate_c_vep itself was already correctly reproducible when y is left to be generated internally (i.e. by passing n_classes instead of a pre-computed y)
• Fixed gammatone in envelope not applying its lowpass filter to the envelope at all (filtfilt was called with the denominator coefficients replaced by 1, i.e. an FIR-only pass), and switched that filter to second-order sections (sosfiltfilt) since the transfer-function (b/a) form of the actual (high-order) filter is numerically unstable and produced NaN once the filter was applied at all
• Fixed DistributionStopping in stopping swapping the wrong axis when moving the target class's score to index 0 before fitting the non-target score distribution (trained=True), which corrupted every fitted distribution and could raise an IndexError
• Fixed BayesStopping in stopping mutating its own target_pf/target_pd parameters as a side effect of predict() (methods bds1/bds2), which meant repeated predict() calls could return different results and clone()/get_params() no longer reflected the original estimator after a prediction; the (per-prediction) target adjustment is now local instead, and reported with warnings.warn instead of print
• Fixed BayesStopping in stopping (method="bds2") raising an unguarded IndexError when no segment jointly satisfies both the target_pf and target_pd constraints; now falls back to the most conservative (last) segment
• Fixed CriterionStopping in stopping silently producing nan scores (and thus a meaningless stop_time_) when n_trials < n_folds left some cross-validation folds with no test data; now raises an assertion instead
• Fixed topoplot in plotting raising a ValueError when reading a .loc file with a trailing newline
• Fixed optimize_layout_incremental in stimulus using the unseeded global np.random.permutation for its random initial layouts, unlike every other randomized function in the library; added a random_state parameter
• Fixed minor internal consistency issues: AggregateGate/DifferenceGate in gates now use the same ClassifierMixin, BaseEstimator MRO order as every other estimator in the library; CriterionStopping.predict in stopping now returns int64 (not float64) for unstopped (-1) trials, matching every other stopping class; DistributionStopping in stopping now implements __sklearn_is_fitted__ instead of checking a private attribute directly through check_is_fitted, matching Ensemble in classifiers
• Fixed stimplot's upsample docstring in plotting, copy-pasted from eventplot and referring to a non-existent "event time-series"
• Fixed eventplot in plotting not validating that events (if provided) has one name per row of E, unlike the equivalent labels check in stimplot
• Fixed the documentation CI workflow committing and pushing to gh-pages on pull requests as well as pushes to main; the build now only deploys on push, and still runs as a build-only check on pull requests
• Fixed test_correlation_faster_corrcoef in the test suite being a wall-clock timing comparison that could fail under CI load for reasons unrelated to the implementation; it is now skipped by default and kept for manual benchmarking only
• Fixed pinv in utilities computing U @ diag(1/d) @ Vh instead of the true Moore-Penrose pseudo-inverse Vh.T @ diag(1/d) @ U.T; both are equivalent (and both were already correct) for the symmetric covariance matrices pinv is used on internally in CCA of transformers, but the old formula silently returned a wrong-shaped, mathematically incorrect result for any general (non-symmetric or non-square) matrix
• Improved performance of euclidean in utilities (vectorized, was an O(n_A * n_B) Python loop), decoding_matrix and encoding_matrix in utilities (avoid computing and discarding the wrapped-around part of np.roll on every window), the inverse square root in CCA of transformers (uses the symmetric eigendecomposition instead of the general-purpose scipy.linalg.sqrtm, which also removes the need to discard spurious imaginary numerical noise with np.real), is_gold_code in stimulus (replaced the O(n_classes^2 * n_bits) loop of np.roll calls with a single vectorized correlation over all circular shifts, gathered by indexing into two concatenated code cycles), transform in CCA of transformers for 3D input (batched matmul directly on the 3D array instead of a transpose+reshape that forced a full copy of the data on every call), _compute_difference_scores in DifferenceGate of gates (replaced the Python double loop over class pairs with a single np.triu_indices gather), and topoplot in plotting (replaced the Python double loop over the 300x300 interpolation grid that masked points outside the head radius with a single vectorized broadcast comparison)
• Fixed decision_function/predict in eCCA/rCCA of classifiers silently returning a meaningless, always-the- same-class prediction (argmax over a NaN or all-equal-score row) instead of raising, when running=True was called with a zero-sample chunk on the very first call of a sequence (before any real data had been observed); now asserts at least 1 sample is available before the first score can be computed. A zero-sample chunk after real data has already been observed remains a well-defined no-op, as intended

Version 1.8.3 (21-05-2025)

Added

• Added approach in BayesStopping of stopping

Changed

• Refactor rCCA of classifiers

Fixed

• Fixed astype in all modules
• Fixed decoding_matrix in rCCA of classifiers only called if required
• Fixed encoding_stride in rCCA of classifiers to allow list input like encoding_length
• Fixed docs

Version 1.8.2 (16-04-2025)

Added

Changed

• Changed stride in encoding_matrix of utilities to allow list input like length

Fixed

• Fixed cca_channels in eCCA of classifiers
• Fixed lags=[...] and ensemble=True combination in eCCA of classifiers

Version 1.8.1 (11-03-2025)

Added

• Added labels to stimplot in plotting

Changed

• Changed pinv in utilities to work with non-square matrices

Fixed

• Fixed array encoding_length of rCCA in classifiers
• Fixed smooth_width of CriterionStopping in stopping
• Fixed stop_time_ of CriterionStopping in stopping
• Fixed gamma_x and gamma_y regularization of CCA in transformers

Version 1.8.0 (08-11-2024)

Added

• Added min_time to stopping methods in stopping
• Added max_time to CriterionStopping in stopping

Changed

Fixed

• Fixed fit exception in DistributionStopping in stopping

Version 1.7.0 (22-10-2024)

Added

• Added tmin to encoding_matrix in utilities
• Added tmin to rCCA in classifiers

Changed

Fixed

Version 1.6.1 (10-10-2024)

Added

• Added find_neighbours and find_worst_neighbour to utilities
• Added optimize_subset_clustering to stimulus
• Added optimize_layout_incremental to stimulus
• Added stimplot to plotting

Changed

• Changed order of tutorials and examples

Fixed

• Fixed max_time in all stopping classes to deal with "partial" segments

Version 1.5.0 (30-09-2024)

Added

• Added ValueStopping to stopping
• Added parameter distribution to DistributionStopping in stopping

Changed

• Changed envelope_rms to rms in envelope
• Changed envelope_gammatone to gammatone in envelope
• Changed BetaStopping in stopping to DistributionStopping

Fixed

• Fixed default CCA in transformers to inv, not pinv
• Fixed seed for make_m_sequence and make_gold_codes in stimulus to not be full zeros

Version 1.4.1 (19-07-2024)

Added

Changed

Fixed

• Fixed default CCA in transformers to inv, not pinv

Version 1.4.0 (15-07-2024)

Added

• Added pinv to utilities
• Added alpha_x to CCA in tranformers
• Added alpha_y to CCA in tranformers
• Added alpha_x to eCCA in classifiers
• Added alpha_t to eCCA in classifiers
• Added alpha_x to rCCA in classifiers
• Added alpha_m to rCCA in classifiers
• Added squeeze_components to rCCA, eCCA, eTRCA in `classifiers'

Changed

• Changed numpy typing of np.ndarray to NDArray
• Changed cca_ and trca_ attributes to be list always in eCCA, rCCA and eTRCA
• Changed scipy.linalg.inv to pyntbci.utilities.pinv in CCA of transformers
• Changed decision_function and predict of classifiers to return without additional dimension for components if n_components=1 and squeeze_components=True, both of which are defaults

Fixed

Version 1.3.3 (01-07-2024)

Added

Changed

Fixed

• Fixed components bug in decision_function of eCCA in classifiers

Version 1.3.2 (23-06-2024)

Added

• Added cov_estimator_t to eCCA in classifiers

Changed

• Changed separate covariance estimators for data and templates in eCCA of classifiers

Fixed

Version 1.3.1 (23-06-2024)

Added

Changed

Fixed

• Fixed zero division eventplot in plotting
• Fixed event order duration event event_matrix in utilities

Version 1.3.0 (18-06-2024)

Added

• Removed gating of rCCA in classifiers
• Removed _score methods in classifiers
• Added n_components in eCCA in classifiers
• Added n_components in eTRCA in classifiers

Changed

• Changed "bes" to "bds" in BayesStopping in stopping in line with publication
• Changed lx and ly to gamma_x and gamma_y iof eCCA in classifiers
• Changed gating to gates
• Changed TRCA in transformers to deal with one-class data only
• Changed _get_T to get_T in all classifiers

Fixed

Version 1.2.0 (18-04-2024)

Added

Changed

• Changed lx of rCCA in classifiers to gamma_x, which ranges between 0-1, such that the parameter represents shrinkage regularization
• Changed ly of rCCA in classifiers to gamma_m, which ranges between 0-1, such that the parameter represents shrinkage regularization
• Changed lx of CCA in transformers to gamma_x, which ranges between 0-1, such that the parameter represents shrinkage regularization
• Changed ly of CCA in transformers to gamma_y, which ranges between 0-1, such that the parameter represents shrinkage regularization

Fixed

Version 1.1.0 (17-04-2024)

Added

• Added envelope module containing envelope_gammatone and envelope_rms functions
• Added CriterionStopping to stopping for some static stopping methods

Changed

• Changed default value of encoding_length in rCCA of classifiers of 0.3 to None, which is equivalent to 1 / fs

Fixed

• Fixed variable fs of type np.ndarray instead of int in examples, tutorials, and pipelines
• Fixed double call to decoding_matrix in fit of rCCA in classifiers

Version 1.0.1 (26-03-2024)

Added

• Added set_stimulus_amplitudes for rCCA in classifiers

Changed

Fixed

• Fixed dependency between stimulus and amplitudes in rCCA of classifiers

Version 1.0.0 (22-03-2024)

Added

• Added variable decoding_length of rCCA in classifier controlling the length of a learned spectral filter
• Added variable decoding_stride of rCCA in classifier controlling the stride of a learned spectral filter
• Added function decoding_matrix in utilities to phase-shit the EEG data maintaining channel-prime ordering
• Added variable encoding_stride of rCCA in classifier controlling the stride of a learned temporal response
• Added module gating with gating functions, for instance for multi-component or filterbank analysis
• Added variable gating of rCCA in classifier to deal with multiple CCA components
• Added variable gating of Ensemble in classifier, for example to deal with a filterbank

Changed

• Changed variable codes of rCCA in classifiers to stimulus
• Changed variable transient_size of rCCA in classifiers to encoding_length
• Changed class FilterBank in classifiers to Ensemble
• Changed function structure_matrix in utilities to encoding_matrix

Fixed

• Fixed several documentation issues

Version 0.2.5 (29-02-2024)

Added

• Added function eventplot in plotting to visualize an event matrix
• Added variable running of covariance in utilities to do incremental running covariance updates
• Added variable running of CCA in transformers to use a running covariance for CCA
• Added variable cov_estimator_x and cov_estimator_m of rCCA in classifiers to change the covariance estimator
• Added event definitions "on", "off" and "onoff" for event_matrix in utilities

Changed

• Changed the CCA optimization to contain separate computations for Cxx, Cyy and Cxy
• Changed the CCA to allow separate BaseEstimators for Cxx and Cyy

Fixed

• Fixed zero-division in itr in utilities

Version 0.2.4

Added

• Added CCA cumulative/incremental average and covariance
• Added amplitudes (e.g. envelopes) in structure_matrix of utilities
• Added max_time to classes in stopping to allow a maximum stopping time for stopping methods
• Added brainamp64.loc to capfiles
• Added plt.show() in all examples

Changed

Fixed

Version 0.2.3

Added

Changed

• Changed example pipelines to include more examples and explanation
• Changed tutorial pipelines to include more examples and explanation

Fixed

• Fixed several documentation issues

Version 0.2.2

Added

• Added class TRCA to transformers
• Added class eTRCA to classifiers
• Added parameter ensemble to classes in classifiers to allow a separate spatial filter per class

Changed

• Changed package name from PyNT to PyntBCI to avoid clash with existing pynt library
• Changed filter order in filterbank of utilities to be optimized given input parameters

Fixed

• Fixed issue in rCCA of classifiers causing novel events in structure matrix when "cutting cycles"
• Fixed correlation to not contain mutable input variables

Version 0.2.1

Added

• Added tests
• Added tutorials

Changed

• Changed rCCA to work with non-binary events instead of binary only

Fixed

Version 0.2.0

Added

• Added dynamic stopping: classes MarginStopping, BetaStopping, and BayesStopping in module stopping
• Added value inner for variable score_metric in 'classifiers'

Changed

• Changed all data shapes from (channels, samples, trials) to (trials, channels, samples)
• Changed all codes shapes from (samples, classes) to (classes, samples)
• Changed all decision functions to similarity, not distance (e.g., Euclidean), to always maximize

Fixed

• Fixed zero-mean templates in eCCA and rCCA of classifiers

Version 0.1.0

Added

• Added Filterbank to classifiers

Changed

• Changed classifiers all have predict and decision_function methods in classifiers

Fixed

Version 0.0.2

Added

Changed

• Changed CCA method from sklearn to custom covariance method

Fixed

Version 0.0.1

Added

• Added eCCA template metrics: average, median, OCSVM
• Added eCCA spatial filter options: all channels or subset

Changed

Fixed

Version 0.0.0

Added

• Added CCA in transformers
• Added rCCA in classifiers
• Added eCCA in classifier

Changed

Fixed