PyntBCI
The Python Noise-Tagging Brain-Computer interfacing (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 [mar2021].
When using PyntBCI, please reference at least one of the following articles: [thi2015], [thi2021], [thi2025].
Installation
To install PyntBCI, use:
pip install pyntbci
Getting started
Various tutorials and example analysis pipelines are provided in the tutorials/ (under Getting Started) and examples/ (under Examples) folder, which operate on limited preprocessed data as provided with PyNTBCI. Furthermore, please find various pipelines for several of the datasets referenced below in the pipelines/ folder.
References
Thielen, J. (2025). Addressing BCI inefficiency in c-VEP-based BCIs: A comprehensive study of neurophysiological predictors, binary stimulus sequences, and user comfort. BPEX. doi: 10.1088/2057-1976/ade316
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. JNE. doi: 10.1088/1741-2552/abecef
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
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. doi: 10.1371/journal.pone.0133797
Datasets
On the Radboud Data Repository (RDR):
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
Ahmadi et al. (2019) Sensor tying. DOI: 10.34973/ehq6-b836
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
On Mother of all BCI Benchmarks (MOABB):
Contact
Jordy Thielen (jordy.thielen@donders.ru.nl)