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Cycle-by-cycle analyses of neural oscillations.

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Version 1.0.0 is not compatible with prior releases.

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bycycle is a python implementation of a cycle-by-cycle approach to analyzing neural oscillations (Cole & Voytek, J Neurophysiol 2019). 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 gained traction recently ( Jones, 2016). Therefore, we only analyze oscillatory properties when there is indeed an oscillation.

A full description of the method and approach is available in the paper below.


If you use this code in your project, please cite:

Cole SR & Voytek B (2019) Cycle-by-cycle analysis of neural oscillations. J Neurophysiol
122:2, 849-861. doi:

Direct Link:


Documentation for bycycle is available on the documentation site.

This documentation includes:

  • Tutorials: with a step-by-step guide through the approach and how to apply it
  • Examples: demonstrating an example analysis and use case
  • API list: which lists and describes all the code and functionality available in the module


bycycle is written in Python, and is tested to work with Python 3.5.

It has the following dependencies:


Stable Version

To install the latest stable release, you can use pip:

$ pip install bycycle

ByCycle can also be installed with conda, from the conda-forge channel:

$ conda install -c conda-forge bycycle

Development Version

To get the lastest, development version, you can get the code using git:

$ git clone

To install this cloned copy, move into the directory you just cloned, and run:

$ pip install .

Editable Version

To install an editable, development version, move into the directory you cloned and install with:

$ pip install -e .


The main function in bycycle is compute_features, which takes a time series and some parameters as inputs and returns a table of features for each cycle. Consider having a 1-dimensional numpy array, sig, which is a neural signal time series sampled at 1000 Hz (fs) that contains an alpha (8-12 Hz, f_range) oscillation. We can compute the table of cycle features with the following:

from neurodsp.sim import sim_bursty_oscillation
from bycycle.features import compute_features

fs = 1000
f_range = (8, 12)

sig = sim_bursty_oscillation(10, fs, freq=10)
df_features = compute_features(sig, fs, f_range)

It’s necessary to note that the above compute_features command used default parameters to localize extrema and detect bursts of oscillations. However, it is important to knowledgeably select these parameters, as described in the algorithm tutorial. The following example and text go over the different potential parameter changes:

threshold_kwargs = {'amp_fraction_threshold': .2,
                    'amp_consistency_threshold': .5,
                    'period_consistency_threshold': .5,
                    'monotonicity_threshold': .8,
                    'min_n_cycles': 3}

narrowband_kwargs = {'n_seconds': .5}

df = compute_features(sig, fs, f_range, center_extrema='trough',
                      burst_method='cycles', threshold_kwargs=threshold_kwargs,
                      find_extrema_kwargs={'filter_kwargs': narrowband_kwargs})
  • center_extrema determines how the cycles are segmented. ‘T’ indicates the center extrema is a trough, so cycles are segmented peak-to-peak.
  • burst_method selects which method for burst detection is used. The ‘cycles’ option uses features of adjacent cycles in order to detect bursts (e.g. period consistency, see next item). The ‘amp’ option uses an amplitude threshold to determine the cycles that are part of an oscillatory burst.
  • threshold_kwargs set the keyword arguments for the burst detection functions. For the cycles method, there are 5 keyword arguments (see the end of the algorithm tutorial for advice on choosing these parameters).
  • find_extrema_kwargs set the keyword arguments for the function used to localize peaks and troughs. Most notably, you can change the duration of the bandpass filter (N_seconds) used during extrema localization (see section 1 of the algorithm tutorial)

DataFrame Output

The output of bycycle is a pandas.DataFrame, a table like the one shown below (with many columns, so it is split into two images).

Each row of this table corresponds to an individuals segment of the signal, or a putative cycle of the rhythm of interest.

Some of the columns include:

  • sample_peak: the sample of the signal at which the peak of this cycle occurs
  • period: period of the cycle
  • time_peak: duration of the peak period
  • volt_amp: amplitude of this cycle, average of the rise and decay voltage
  • time_rdsym: rise-decay symmetry, the fraction of the cycle in the rise period (0.5 is symmetric)
  • time_ptsym: peak-trough symmetry, the fraction of the cycle in the peak period (0.5 is symmetric)
  • period_consistency: consistency between the periods of the adjacent cycles, used in burst detection
  • is_burst: indicator if the cycle is part of an oscillatory burst

The features in this table can then go on to be analyzed, as demonstrated in the resting-state data tutorial and the data example. For example, we may be interested in the distribution of rise-decay symmetry values in a resting state recording, shown below.

Burst Detection Results


Supported by NIH award R01 GM134363


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