spectral_connectivity is a python software package that computes frequency-domain brain connectivity measures such as coherence, spectral granger causality, and the phase lag index using the multitaper Fourier transform. Although there are other python packages that do this (see nitime and MNE-Python), spectral has several differences:

• it is designed to handle multiple time series at once
• it caches frequently computed quantities such as the cross-spectral matrix and minimum-phase-decomposition, so that connectivity measures that use the same processing steps can be more quickly computed.
• it decouples the time-frequency transform and the connectivity measures so that if you already have a preferred way of computing Fourier coefficients (i.e. from a wavelet transform), you can use that instead.
• it implements the non-parametric version of the spectral granger causality in Python.
• it implements the canonical coherence, which can efficiently summarize brain-area level coherences from multielectrode recordings.
• easier user interface for the multitaper fourier transform

See the notebooks (#1, #2) for more information on how to use the package.

See the documentation here.

Functional

1. coherency
2. canonical_coherence
3. imaginary_coherence
4. phase_locking_value
5. phase_lag_index
6. weighted_phase_lag_index
7. debiased_squared_phase_lag_index
8. debiased_squared_weighted_phase_lag_index
9. pairwise_phase_consistency

Directed

10. directed_transfer_function
11. directed_coherence
12. partial_directed_coherence
13. generalized_partial_directed_coherence
14. direct_directed_transfer_function
15. group_delay
16. phase_lag_index
17. pairwise_spectral_granger_prediction

spectral_connectivity requires:

• python=3.5
• numpy
• matplotlib
• scipy
• xarray

See environment.yml for the most current list of dependencies.

pip install spectral_connectivity

or

conda install -c edeno spectral_connectivity

If you want to make contributions to this library, please use this installation.

1. Install miniconda (or anaconda) if it isn't already installed. Type into bash (or install from the anaconda website):

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
bash miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
hash -r

2. Clone the repository to your local machine (.../spectral_connectivity) and install the anaconda environment for the repository. Type into bash:

conda update -q conda
conda info -a
conda env create -f environment.yml
source activate spectral_connectivity
python setup.py develop

from spectral_connectivity import Multitaper, Connectivity

m = Multitaper(time_series=signals,
               sampling_frequency=sampling_frequency,
               time_halfbandwidth_product=time_halfbandwidth_product,
               time_window_duration=0.060,
               time_window_step=0.060,
               start_time=time[0])
c = Connectivity.from_multitaper(m)

coherence = c.coherence_magnitude()
weighted_phase_lag_index = c.weighted_phase_lag_index()
canonical_coherence = c.canonical_coherence(brain_area_labels)

We hope to take advantage of the labeled data of the xarray package.