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README.rst

rERPy is a Python toolkit for doing ERP/ERF analysis of brainwave data (EEG, MEG), using both traditional averaging-based ERP/ERF estimation, and a fancy new regression-based technique for ERP/ERF estimation, which we call rERP/rERF. rERPs can do anything ERPs can do -- in fact, ERPs are special cases of rERPs; every ERP is also a rERP. But rERPs are much more powerful. rERPs make it straightforward to analyze experimental designs that use a mix of categorical and continuous manipulations, even when these manipulations are partially confounded or produce non-linear effects, and they can separate out overlapping waveforms timelocked to temporally adjacent events. They can even do all these things at the same time. Nonetheless, they are relatively simple to use.

https://travis-ci.org/rerpy/rerpy.png?branch=master https://coveralls.io/repos/rerpy/rerpy/badge.png?branch=master
Documentation:
http://rerpy.readthedocs.org/
Downloads:
not yet
Dependencies:
  • Python 2.7 (not Python 3 yet, sorry -- patches accepted!)
  • numpy
  • scipy
  • pandas
  • patsy

If you're starting from scratch (not previously a Python user), then we recommend installing a scientific Python distribution.

Optional dependencies:
  • nose: needed to run tests
Install:
probably not a great idea yet
Mailing list:
not yet, but in the mean time you can hassle nathaniel.smith@ed.ac.uk
Code and bug tracker:
https://github.com/rerpy/rerpy
License:
GPLv2+, see LICENSE.txt for details.

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rERPy is a Python library for doing ERP/ERF and rERP/rERF analysis of brainwaves

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