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 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
• 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 (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.

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