Connectivity analysis in music perception of EEG in relation to music
: The project mostly consists of development code, although some modules and functions are already working.
Automatic documentation is available online.
This is mostly a translation of Python code Notebook from the NoiseTools toolbox by Alain de Cheveigné. It builds on an initial python implementation by Pedro Alcocer.
If you use this code, you should cite the relevant methods from the original articles:
[1] de Cheveigné, A. (2019). ZapLine: A simple and effective method to remove power line artifacts.
NeuroImage, 116356. https://doi.org/10.1016/j.neuroimage.2019.116356
[2] de Cheveigné, A. et al. (2019). Multiway canonical correlation analysis of brain data.
NeuroImage, 186, 728–740. https://doi.org/10.1016/j.neuroimage.2018.11.026
[3] de Cheveigné, A. et al. (2018). Decoding the auditory brain with canonical component analysis.
NeuroImage, 172, 206–216. https://doi.org/10.1016/j.neuroimage.2018.01.033
[4] de Cheveigné, A. (2016). Sparse time artifact removal.
Journal of Neuroscience Methods, 262, 14–20. https://doi.org/10.1016/j.jneumeth.2016.01.005
[5] de Cheveigné, A., & Parra, L. C. (2014). Joint decorrelation, a versatile tool for multichannel
data analysis. NeuroImage, 98, 487–505. https://doi.org/10.1016/j.neuroimage.2014.05.068
[6] de Cheveigné, A. (2012). Quadratic component analysis.
NeuroImage, 59(4), 3838–3844. https://doi.org/10.1016/j.neuroimage.2011.10.084
[7] de Cheveigné, A. (2010). Time-shift denoising source separation.
Journal of Neuroscience Methods, 189(1), 113–120. https://doi.org/10.1016/j.jneumeth.2010.03.002
[8] de Cheveigné, A., & Simon, J. Z. (2008a). Denoising based on spatial filtering.
Journal of Neuroscience Methods, 171(2), 331–339. https://doi.org/10.1016/j.jneumeth.2008.03.015