PyRASA - Spectral parameterization in python based on IRASA#

Project Status: Active – The project has reached a stable, usable state and is being actively developed. License Checked with mypy Coverage Status

PyRASA is a Python library designed to separate and parametrize aperiodic (fractal) and periodic (oscillatory) components in time series data based on the IRASA algorithm (Wen & Liu, 2016).

Features#

  • Aperiodic and Periodic Decomposition: Utilize the IRASA algorithm to decompose power spectra into aperiodic and periodic components, enabling better interpretation of neurophysiological signals.

  • Time Resolved Spectral Parametrization: Perform time resolved spectral parametrization, allowing you to track changes in spectral components over time.

  • Support for Raw and Epoched MNE Objects: PyRASA provides functions designed for both continuous (Raw) and event-related (Epochs) data, making it versatile for various types of EEG/MEG analyses.

  • Consistent Ontology: PyRASA uses the same jargon to label parameters as specparam, the most commonly used tool to parametrize power spectra, to allow users to easily switch between tools depending on their needs, while keeping the labeling of features consistent.

  • Custom Aperiodic Fit Models: In addition to the built-in “fixed” and “knee” models for aperiodic fitting, users can specify their custom aperiodic fit functions, offering flexibility in how aperiodic components are modeled.

Example Usage#

Decompose spectra into periodic and aperiodic components:

from pyrasa.irasa import irasa

irasa_out = irasa(sig,
                  fs=fs,
                  band=(.1, 200),
                  psd_kwargs={'nperseg': duration*fs,
                              'noverlap': duration*fs*overlap
                             },
                  hset_info=(1, 2, 0.05))
Example knee image

Extract periodic parameters:

irasa_out.get_peaks()

ch_name

cf

bw

pw

0

9.5

1.4426

0.4178

Extract aperiodic parameters:

irasa_out.fit_aperiodic_model(fit_func='knee').aperiodic_params

Offset

Knee

Exponent_1

Exponent_2

fit_type

Knee Frequency (Hz)

tau

ch_name

1.737e-16

60.94

0.0396

1.4727

knee

14.131

0.0113

0

And the goodness of fit:

irasa_out.fit_aperiodic_model(fit_func='knee').gof

mse

r_squared

BIC

AIC

fit_type

ch_name

0.000051

0.999751

-3931.840

-3947.806

knee

0

How to Contribute#

Contributions to PyRASA are welcome! Whether it’s raising issues, improving documentation, fixing bugs, or adding new features, your help is appreciated.

To file bug reports and/or ask questions about this project, please use the Github issue tracker.

Please refer to the CONTRIBUTING.md file for more information on how to get involved.

Reference#

If you are using IRASA, please cite the smart people who came up with the algorithm:

Wen, H., & Liu, Z. (2016). Separating fractal and oscillatory components in the power spectrum of neurophysiological signal. Brain Topography, 29, 13-26. https://doi.org/10.1007/s10548-015-0448-0

If you are using PyRASA, it would be nice if you could additionally cite us (whenever the paper is finally ready):

Schmidt F., Hartmann T., & Weisz, N. (2049). PyRASA - Spectral parameterization in python based on IRASA. SOME JOURNAL THAT LIKES US

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