Harmoni: a Novel Method for Eliminating Spurious Neuronal Interactions due to the Harmonic Components in Neuronal Data
(c) Mina Jamshidi ([email protected]) @ Neurolgy Dept, MPI CBS, 2021 https://github.com/minajamshidi (c) please cite the above paper in case of adaptation and/or using this code for your research
Investigating CFS in Magneto- and Electroencephalography (MEG/EEG) is hampered by the presence of spurious neuronal interactions due to non-sinusoidal waveshape of brain oscillations. Such waveshape gives rise to the presence of oscillatory harmonics mimicking genuine neuronal oscillations. Until recently, however, there has been no methodology for removing these harmonics from neuronal data.
Here, we introduce a novel method (called HARMOnic miNImization - Harmoni) that removes the signal components which can be harmonics of a non-sinusoidal signal. Harmoni’s working principle is based on the presence of CFS between harmonic components and the fundamental component of a non-sinusoidal signal.
Using Harmoni, one can build conenctivity maps, in which the effect of harmonics are minimized.
You should first install harmoni:
$ pip install harmoniHere, you can see how the scrpts correspond to the manuscript figures:
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figure 2: sawtooth_toy.py
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figure 3: harmoni_blockdiagram.py
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figure 8: simulations_toys.py
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figure 9: proof_of_concept.py
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figure 10: realisticsimulation_results.py
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figure 11: lemon_nonsin_source_exp.py
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figures 12, 13, 14: lemon_data_analysis.py
additionally:
- computing the connectivity of LEMON data: lemon_conn_bandpass.py
Oct. 2021 Codes contributing to the first bioRxiv preprint at https://doi.org/10.1101/2021.10.06.463319
Feb. 2022The current version is unstable and some scripts may raise errors of unknown functions etc. Soon the repository will be coherent again!