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.
- Documentation:
- 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:
- License:
GPLv2+, see LICENSE.txt for details.
