cycle-by-cycle analysis of neural oscillations
bycycle is a python implementation of a cycle-by-cycle approach to analyzing neural oscillations (see Cole & Voytek, 2018, biorxiv). This approach quantifies features of neural oscillations in the time domain as opposed to the frequency domain. Rather than applying narrowband filters and other methods that utilize a sinusoidal basis, this characterization segments a recording into individual cycles and directly measures each of their properties including amplitude, period, and symmetry. This is most advantageous for analyzing the waveform shape properties of neural oscillations, but it may also provide advantages for studying traditional amplitude and frequency effects, as well. It also implements burst detection, which has been gaining traction recently (see e.g. Jones, 2016, COiN) so that we only analyze oscillatory properties when there is indeed an oscillation.
If you use this code in your project, please cite:
Cole SR & Voytek B (2018) Cycle-by-cycle analysis of neural oscillations. bioRxiv, 302000. doi: https://doi.org/10.1101/302000
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