A package of modules to process and analyze neural recordings as individual voltage time series. The primary purpose of this library is to serve as the shared codebase for the Voytek Lab, but we welcome anyone's use and contributions.
This package has been tested on python 3.4, 3.5, and 3.6 with the latest Anaconda distribution. Support for python 2 and earlier versions of python 3 is not guaranteed.
$ git clone https://github.com/voytekresearch/neurodsp.git
$ pip install neurodsp
- filt : Filter data with bandpass, highpass, lowpass, or notch filters (Tutorial)
- spectral : Compute spectral domain features (PSD and 1/f slope, etc) (Tutorial)
- timefrequency : Estimate instantaneous measures of oscillatory activity (Tutorial)
- shape : Measure the waveform shape of neural oscillations
- cyclefeatures : Compute features of an oscillation on a cycle-by-cycle basis (Tutorial)
- cyclepoints : Identify the extrema and zerocrossings for each cycle (Tutorial)
- phase : Estimate instantaneous phase by interpolating between extrema and zerocrossings (Tutorial)
- swm : Identify recurrent patterns in a signal using sliding window matching (Tutorial)
- burst : Detect bursting oscillators in neural signals (Tutorial)
- sim : Simulate bursting or stationary oscillators with brown noise (Tutorial)
- pac : Estimate phase-amplitude coupling between two frequency bands (Tutorial)
- laggedcoherence : Estimate rhythmicity using the lagged coherence measure (Tutorial)
- numpy
- scipy
- matplotlib
- scikit-learn
- pandas
- pytest (optional)