# PyntBCI
The Python Noise-Tagging Brain-Computer interface (PyntBCI) library is a Python toolbox for the noise-tagging brain-computer interfacing (BCI) project developed at the Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands. PyntBCI contains various signal processing steps and machine learning algorithms for BCIs that make use of evoked responses of the electroencephalogram (EEG), specifically code-modulated responses such as the code-modulated visual evoked potential (c-VEP). For a constructive review of this 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](https://doi.org/10.1088/1741-2552/ac38cf)
## 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 limited preprocessed data as provided with PyntBCI. Furthermore, please find various pipelines for several open-access datasets below in the `pipelines/` folder.
## 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](https://doi.org/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](https://doi.org/10.1088/1741-2552/abecef)
## Contact
* Jordy Thielen (jordy.thielen@donders.ru.nl)
## 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 1.0.0
### Added
- Variable `decoding_length` of `rCCA` in `classifier` controlling the length of a learned spectral filter
- Variable `decoding_stride` of `rCCA` in `classifier` controlling the stride of a learned spectral filter
- Function `decoding_matrix` in `utilities` to phase-shit the EEG data maintaining channel-prime ordering
- Variable `encoding_stride` of `rCCA` in `classifier` controlling the stride of a learned temporal response
- Module `gating` with gating functions, for instance for multi-component or filterbank analysis
- Variable `gating` of `rCCA` in `classifier` to deal with multiple CCA components
- Variable `gating` of `Ensemble` in `classifier`, for example to deal with a filterbank
### Changed
- Variable `codes` of `rCCA` in `classifiers` is renamed to `stimulus`
- Variable `transient_size` of `rCCA` in `classifiers` is renamed to `encoding_length`
- Class `FilterBank` in `classifiers`, is renamed to `Ensemble`
- Function `structure_matrix` in `utilities` is renamed to `encoding_matrix`
### Fixed
- Several documentation issues
## Version 0.2.5 (29-02-2024)
### Added
- Function `eventplot` in `plotting` to plot an event matrix
- Variable `running` of `covariance` in `utilities` to do incremental running covariance updates
- Variable `running` of `CCA` in `transformers` to use a running covariance for CCA
- Variable `cov_estimator_x` and `cov_estimator_x` of `rCCA` in `classifiers` to change the covariance estimator
- Event definitions "on", "off" and "onoff" for `event_matrix` in `utilities`
### Changed
- CCA separate computation for Cxx, Cyy and Cxy
- CCA separate estimators for Cxx and Cyy
### Fixed
- ITR calculation zero-division
## Version 0.2.4
### Added
- CCA cumulative/incremental average and covariance
- Amplitudes (e.g. envelopes) in structure matrix
- Maximum stopping time (`max_time`) for stopping methods
- brainamp64.loc
- A plt.show() in all examples
### Changed
### Fixed
- ITR calculation zero-division
## Version 0.2.3
### Added
### Changed
- Improved documentation
- Improved example pipelines
- Improved tutorial
### Fixed
## Version 0.2.2
### Added
- TRCA transformer
- eTRCA classifier
- Ensemble (`ensemble`) option (i.e., a spatial filter per class) for classifiers
### Changed
- Package name change of PyNT to PyntBCI
- Filterbank order optimized given parameters
### Fixed
- Issue causing novel events in M when "cutting cycles"
- Correlation does not change mutable input variables
## Version 0.2.1
### Added
- Tests
- Tutorial
### Changed
- Non-binary events for rCCA
### Fixed
## Version 0.2.0
### Added
- Dynamic stopping: margin, beta, Bayes
- Inner score metric
### Changed
- All data shapes: trials, channels, samples
- All codes shapes: classes, samples
- Changed all decision functions to similarity, not distance (e.g., Euclidean), to always maximize
### Fixed
- Zero-mean templates in eCCA and rCCA
## Version 0.1.0
### Added
- Filterbank classifier
### Changed
- Classifiers all have predict() and decision_function()
### Fixed
## Version 0.0.2
### Added
### Changed
- CCA method changed from sklearn to covariance method
### Fixed
## Version 0.0.1
### Added
- eCCA template metrics: average, median, OCSVM
- eCCA spatial filter options: all channels or subset
### Changed
### Fixed
## Version 0.0.0
### Added
- CCA transformer
- rCCA classifier
- eCCA classifier
### Changed
### Fixed
Raw data
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"description": "# PyntBCI\n\nThe Python Noise-Tagging Brain-Computer interface (PyntBCI) library is a Python toolbox for the noise-tagging brain-computer interfacing (BCI) project developed at the Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands. PyntBCI contains various signal processing steps and machine learning algorithms for BCIs that make use of evoked responses of the electroencephalogram (EEG), specifically code-modulated responses such as the code-modulated visual evoked potential (c-VEP). For a constructive review of this field, see:\n\n* Mart\u00ednez-Cagigal, V., Thielen, J., Santamar\u00eda-V\u00e1zquez, E., P\u00e9rez-Velasco, S., Desain, P., & Hornero, R. (2021). Brain\u2013computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review. Journal of Neural Engineering. DOI: [10.1088/1741-2552/ac38cf](https://doi.org/10.1088/1741-2552/ac38cf)\n\n## Installation\n\nTo install PyntBCI, use:\n\n\tpip install pyntbci\n\n## Getting started\n\nVarious tutorials and example analysis pipelines are provided in the `tutorials/` and `examples/` folder, which operate on limited preprocessed data as provided with PyntBCI. Furthermore, please find various pipelines for several open-access datasets below in the `pipelines/` folder.\n\n## Referencing\n\nWhen using PyntBCI, please reference at least one of the following:\n\n* 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](https://doi.org/10.1371/journal.pone.0133797)\n* Thielen, J., Marsman, P., Farquhar, J., & Desain, P. (2021). From full calibration to zero training for a code-modulated visual evoked potentials for brain\u2013computer interface. Journal of Neural Engineering, 18(5), 056007. DOI: [10.1088/1741-2552/abecef](https://doi.org/10.1088/1741-2552/abecef)\n\n## Contact\n\n* Jordy Thielen (jordy.thielen@donders.ru.nl)\n\n## Licensing\n\nPyntBCI is licensed by the BSD 3-Clause License:\n\nCopyright (c) 2021, Jordy Thielen All rights reserved.\n\nRedistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.\n2. 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.\n3. 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.\n\nTHIS 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.\n\n# Changelog\n\n## Version 1.0.0\n\n### Added\n\n- Variable `decoding_length` of `rCCA` in `classifier` controlling the length of a learned spectral filter\n- Variable `decoding_stride` of `rCCA` in `classifier` controlling the stride of a learned spectral filter\n- Function `decoding_matrix` in `utilities` to phase-shit the EEG data maintaining channel-prime ordering\n- Variable `encoding_stride` of `rCCA` in `classifier` controlling the stride of a learned temporal response\n- Module `gating` with gating functions, for instance for multi-component or filterbank analysis\n- Variable `gating` of `rCCA` in `classifier` to deal with multiple CCA components\n- Variable `gating` of `Ensemble` in `classifier`, for example to deal with a filterbank\n\n### Changed\n\n- Variable `codes` of `rCCA` in `classifiers` is renamed to `stimulus`\n- Variable `transient_size` of `rCCA` in `classifiers` is renamed to `encoding_length`\n- Class `FilterBank` in `classifiers`, is renamed to `Ensemble`\n- Function `structure_matrix` in `utilities` is renamed to `encoding_matrix`\n\n### Fixed\n\n- Several documentation issues\n\n## Version 0.2.5 (29-02-2024)\n\n### Added\n\n- Function `eventplot` in `plotting` to plot an event matrix\n- Variable `running` of `covariance` in `utilities` to do incremental running covariance updates\n- Variable `running` of `CCA` in `transformers` to use a running covariance for CCA \n- Variable `cov_estimator_x` and `cov_estimator_x` of `rCCA` in `classifiers` to change the covariance estimator \n- Event definitions \"on\", \"off\" and \"onoff\" for `event_matrix` in `utilities`\n\n### Changed\n\n- CCA separate computation for Cxx, Cyy and Cxy\n- CCA separate estimators for Cxx and Cyy\n\n### Fixed\n\n- ITR calculation zero-division\n\n## Version 0.2.4\n\n### Added\n\n- CCA cumulative/incremental average and covariance\n- Amplitudes (e.g. envelopes) in structure matrix\n- Maximum stopping time (`max_time`) for stopping methods\n- brainamp64.loc\n- A plt.show() in all examples\n\n### Changed\n\n### Fixed\n\n- ITR calculation zero-division\n\n## Version 0.2.3\n\n### Added\n\n### Changed\n\n- Improved documentation\n- Improved example pipelines\n- Improved tutorial\n\n### Fixed\n\n## Version 0.2.2\n\n### Added\n\n- TRCA transformer\n- eTRCA classifier\n- Ensemble (`ensemble`) option (i.e., a spatial filter per class) for classifiers\n\n### Changed\n\n- Package name change of PyNT to PyntBCI\n- Filterbank order optimized given parameters\n\n### Fixed\n\n- Issue causing novel events in M when \"cutting cycles\"\n- Correlation does not change mutable input variables\n\n## Version 0.2.1\n\n### Added\n\n- Tests\n- Tutorial\n\n### Changed\n\n- Non-binary events for rCCA\n\n### Fixed\n\n## Version 0.2.0\n\n### Added\n\n- Dynamic stopping: margin, beta, Bayes\n- Inner score metric\n\n### Changed\n\n- All data shapes: trials, channels, samples\n- All codes shapes: classes, samples\n- Changed all decision functions to similarity, not distance (e.g., Euclidean), to always maximize\n\n### Fixed\n\n- Zero-mean templates in eCCA and rCCA\n\n## Version 0.1.0\n\n### Added\n\n- Filterbank classifier\n\n### Changed\n\n- Classifiers all have predict() and decision_function()\n\n### Fixed\n\n## Version 0.0.2\n\n### Added\n\n### Changed\n\n- CCA method changed from sklearn to covariance method\n\n### Fixed\n\n## Version 0.0.1\n\n### Added\n\n- eCCA template metrics: average, median, OCSVM\n- eCCA spatial filter options: all channels or subset\n\n### Changed\n\n### Fixed\n\n## Version 0.0.0\n\n### Added\n\n- CCA transformer\n- rCCA classifier\n- eCCA classifier\n\n### Changed\n\n### Fixed\n",
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