Version : 0.1.0

Start with the documentation

Brainpipe is a toolbox to analyse neuro-physiological signals. For instance, it's specialised for eeg, seeg and ecog signals. The aim of this toolbox is to extract informations from data [= features], to classify them and to find the best features combination from wide variety of features. Here is the list of the current implemented modules and there respectiv description:

• bpstudy : managed features / file database
• Extract physiological informations using mni/talairach coordonates of electrodes
• feature : extract power, phase, phase-amplitude coupling features
• optimized classification using scikit-learn
• find the optimal combination of features

Create and manage many studies, without carrying of path, variable names or settings. Everything is going to be self organized in the clearest way as possible.

This module give the physiological informations of intracranial recordings. It will return the brodmann area, the gyrus, the lobe and the hemisphere for a given list of electrodes' coordonates. The results should be the same as Talairach Daemon

Extract time resolved features from original signals. Here's the current list of extractable features:

• Filtered signal (using butterworth / bessel / eegfilt filters)
• Power (hilbert / wavelet)
• Phase (based on hilbert transform)
• Phase-amplitude coupling (10 methods) with a variety of normalizations and surrogates computing methods
• Entropy (coming soon)
• Kurtosis (coming soon)
• Fractales, C1 // C2 (very futur)

Classify each time resolved features using parallel computing. There is also several methods to evaluate the statistical significiance of decoding accuracies:

• binomial
• permutations
  • shuffle labels
  • full randomization
  • intra-class shuffling

The classification modules provide the basics classifiers and cross-validations implemented in scikit-learn with an optimization for large array (which is convenient for features classification). The classification module also include:

• Time generalization : generalize the decoding performance of features across time

This module include several well known methods for computing multi-features. It includes :

• Select all the features
• forward / backward / exhaustif features selection
• statistical selection (same methods as in classification module)
• nbest : select nbest features All this methods can be used individually or they can be combined in a sequential way. An other interesting feature, is that for a given set of features, you can define groups and do a multi-features inside each group.

v0.0 compatible with python 3.x only (Still in development, final codes versions in xPOO for instance)

stereotactic electroencephalography, sEEG, iEEG, intracranial, micro-electrodes, ecog, power, phase-amplitude coupling, pac, phase, entropy, permutations, classification, brodmann, python, time generalization