pyntbci 0.2.4

Python Noise-Tagging Brain-Computer Interface (PyntBCI)

PyntBCI

Python Noise-Tagging Brain-Computer interface (PyntBCI) is a Python library for the noise-tagging brain-computer interface (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 [7].

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 the datasets as provided below.

References

[1]: 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

[2]: Thielen, J., Marsman, P., Farquhar, J., & Desain, P. (2017). Re(con)volution: accurate response prediction for broad-band evoked potentials-based brain computer interfaces. In Brain-Computer Interface Research (pp. 35-42). Springer, Cham. DOI: 10.1007/978-3-319-64373-1_4

[3]: Desain, P. W. M., Thielen, J., van den Broek, P. L. C., & Farquhar, J. D. R. (2019). U.S. Patent No. 10,314,508. Washington, DC: U.S. Patent and Trademark Office. Link: here

[4]: Ahmadi, S., Borhanazad, M., Tump, D., Farquhar, J., & Desain, P. (2019). Low channel count montages using sensor tying for VEP-based BCI. Journal of Neural Engineering, 16(6), 066038. DOI: 10.1088/1741-2552/ab4057

[5]: 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

[6]: Verbaarschot, C., Tump, D., Lutu, A., Borhanazad, M., Thielen, J., van den Broek, P., ... & Desain, P. (2021). A visual brain-computer interface as communication aid for patients with amyotrophic lateral sclerosis. Clinical Neurophysiology, 132(10), 2404-2415. DOI: 10.1016/j.clinph.2021.07.012

[7]: 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

[8]: Thielen, J. (2023). Effects of Stimulus Sequences on Brain-Computer Interfaces Using Code-Modulated Visual Evoked Potentials: An Offline Simulation. In International Work-Conference on Artificial Neural Networks (pp. 555-568). Cham: Springer Nature Switzerland. DOI: 10.1007/978-3-031-43078-7_45

Datasets

On the Radboud Data Repository (RDR) (https://data.ru.nl/):

• Thielen et al. (2018) Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing. DOI: 10.34973/1ecz-1232
• Ahmadi et al. (2018) High density EEG measurement. DOI: 10.34973/psaf-mq72
• Ahmadi et al. (2019) Sensor tying. DOI: 10.34973/ehq6-b836
• Thielen et al. (2021) From full calibration to zero training for a code-modulated visual evoked potentials brain computer interface. DOI: 10.34973/9txv-z787

On Mother of all BCI Benchmarks (MOABB) (https://moabb.neurotechx.com/docs/index.html):

• c-VEP dataset from Thielen et al. (2021)

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 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