Core-Developer

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EMD: Empirical Mode Decomposition

The Empirical Mode Decomposition (EMD) package contains Python functions for analysis of non-linear and non-stationary oscillatory time series. EMD implements a family of sifting algorithms, instantaneous frequency transformations, power spectrum construction and single-cycle feature extraction.

Documentation Status EMD License

EMD: Empirical Mode Decomposition and Hilbert-Huang Spectral Analyses in Python
Andrew Quinn, Vitor Lopes-dos-Santos, David Dupret, Anna Nobre & Mark Woolrich (Mar 2021)
The Journal of Open Source Software doi
Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics
Andrew J. Quinn, Vítor Lopes-dos-Santos, Norden Huang, Wei-Kuang Liang, Chi-Hung Juan, Jia-Rong Yeh, Anna C. Nobre, David Dupret & Mark W. Woolrich (Apr 2021)
bioRxiv (Cold Spring Harbor Laboratory) doi
Understanding Harmonic Structures Through Instantaneous Frequency
Marco S. Fabus, Mark W. Woolrich, Catherine W. Warnaby & Andrew J. Quinn (Jan 2022)
IEEE Open Journal of Signal Processing doi
Automatic decomposition of electrophysiological data into distinct non-sinusoidal oscillatory modes
Marco S. Fabus, Andrew J. Quinn, Catherine E. Warnaby & Mark W. Woolrich (Jul 2021)
bioRxiv (Cold Spring Harbor Laboratory) doi
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SAILS: Spectrum Analysis in Linear Systems

SAILS is a python package autoregressive modelling of mutlltivariate time series. Routines are provided for linear model fitting, model validation, power spectrum estimation and connectivity estimation. SAILS also provides functionality for decomposing fitted autoregressive models into a set of data-driven multivariate oscillatory modes.

Documentation Status

SAILS: Spectral Analysis In Linear Systems
Andrew Quinn & Mark Hymers (Mar 2020)
The Journal of Open Source Software doi
Delineating between-subject heterogeneity in alpha networks with Spatio-Spectral Eigenmodes
Andrew J. Quinn, Gary G.R. Green & Mark Hymers (Jul 2021)
NeuroImage doi
The GLM-Spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling
Andrew J Quinn, Lauren Z Atkinson, Chetan Gohil, Oliver Kohl, Jemma Pitt, Catharina Zich, Anna C Nobre & Mark W Woolrich (Nov 2022)
bioRxiv (Cold Spring Harbor Laboratory) doi
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GLMTools

glmtools is a Python package which aims to simplify the definition, creation and reuse of design matrices for General Linear Models. It contains routines for abstractly defining a GLM design before applying this definition to new data to create a design matrix. There are routines for basic linear model fitting, t-stat calculation and non-parametric permutations.

The GLM-Spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling
Andrew J Quinn, Lauren Z Atkinson, Chetan Gohil, Oliver Kohl, Jemma Pitt, Catharina Zich, Anna C Nobre & Mark W Woolrich (Nov 2022)
bioRxiv (Cold Spring Harbor Laboratory) doi
Subjective SES is Associated with Children’s Neurophysiological Response to Auditory Oddballs
Alexander L Anwyl-Irvine, Edwin S Dalmaijer, Andrew J Quinn, Amy Johnson & Duncan E Astle (Dec 2020)
Cerebral Cortex Communications doi
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OSL-Python

The OHBA Software Library is a set of MatLab tools for preprocessing and analysis of magnetoencephalography data. Routines are provided for a range of sensor-space and source space analyses. OSL builds upon the MEG processing tools provided by MNE-Python

EMD License

The GLM-Spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling
Andrew J Quinn, Lauren Z Atkinson, Chetan Gohil, Oliver Kohl, Jemma Pitt, Catharina Zich, Anna C Nobre & Mark W Woolrich (Nov 2022)
bioRxiv (Cold Spring Harbor Laboratory) doi

Contributor

MNE-Python
OSL Matlab
OHBA-Analysis Group
Neuroimaging Analysis Framework
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