.. eeg-ride documentation master file, created by sphinx-quickstart on Mon Jul 22 17:49:34 2024. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. eeg-ride ======== .. image:: https://img.shields.io/pypi/v/eeg-ride :target: https://pypi.org/project/eeg-ride :alt: Latest Version .. image:: https://img.shields.io/pypi/pyversions/eeg-ride.svg :target: https://img.shields.io/pypi/pyversions/eeg-ride :alt: PyPI - Python Version .. image:: https://img.shields.io/github/license/kirstenstark/eeg-ride :target: https://github.com/kirstenstark/eeg-ride/blob/main/LICENSE :alt: License | Separating EEG data into stimulus- and response-related components using Residue Iteration Decomposition (RIDE). Based on the `MATLAB toolbox `_ described in: Ouyang, G., Sommer W., & Zhou, C. (2015). A toolbox for residue iteration decomposition (RIDE)--A method for the decomposition, reconstruction, and single trial analysis of event related potentials. *Journal of Neuroscience Methods*, *250*, 7-21. `https://doi.org/10.1016/j.jneumeth.2014.10.009 `_ One typical application is for correction of speech artifacts as described and validated in: Ouyang, G., Sommer, W., Zhou, C., Aristei, S., Pinkpank, T., & Abdel Rahman, R. (2016). Articulation artifacts during overt language production in event-related brain potentials: Description and correction. *Brain topography*, *29*, 791-813. `https://doi.org/10.1007/s10548-016-0515-1 `_ .. toctree:: :maxdepth: 1 :caption: Contents: installation quickstart examples/speech tips