MNE-compatible package for SSVEP analysis
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A package to analyse MNE-formatted EEG data for steady-state visually evoked potentials (SSVEPs).


pip install git+

As always with pip packages, you can install a "development" version of this package by (forking and) cloning the git repository and installing it via pip install -e /path/to/package. Please do open a pull request if you make improvements.


The docs for this package are at There, you'll find the API and an example notebook.


You should load, preprocess and epoch your data using MNE.

Take a look at a notebook that sets up an SSVEP analysis structure with the example data in this package:

Once you have a data structure of the class Epoch, you can use ssvepy.Ssvep(epoch_data, stimulation_frequency), where stimulation_frequency is the frequency (or list of frequencies) at which you stimulated your participants.

Other input parameters and their defaults are:

  • The following parameters, which are equivalent to the parameters in mne.time_frequency.psd_multitaper:
    • fmin=0.1, the low end of the frequency range
    • fmax=50, the high end of the frequency range
    • tmin=None, the start time of the segment you want to analyse
    • tmax=None, the end time of the segment you want to analyse
  • noisebandwidth=1.0, what bandwidth around a frequency should be used to calculate its signal-to-noise-ratio
  • Whether you want to compute the following nonlinearity frequencies:
  • compute_harmonics=True
  • compute_subharmonics=False
  • compute_intermodulation=True (NB: only when there's more than one input frequency)
  • You can also provide your own Power-spectrum data, if you have worked it out using another method.
    • psd=None The powerspectrum. Needs to be a numpy array with dimensions: (epochs, channels, frequency)
    • freqs=None The frequencys at which the powerspectrum was evaluated. Needs to be a one-dimensional numpy array.

The resulting data has the following attributes:

  • stimulation: a data structure with the following attributes:
    • stimulation.frequencies, stimulation.power, stimulation.snr
  • harmonics, subharmonics, intermodulations: non-linear combination of your input stimulus frequencies, all with the attributes:
    • _.frequencies, _.power, _.snr, _.order
  • psd: the Power-spectrum
  • freqs: the frequencies at which the psd was evaluated

And the following methods:

  • plot_psd(): Plot the power spectrum
  • plot_snr(): Plot the SNR spectrum
  • save(filename): Saves an hdf5 file that can be loaded with ssvepy.load_ssvep(filename) 1

More to come.

1: This package currently uses hierarchical data files (hdf5) because it seems to lend itself to the different data stored in ssvep classes, but I know it's less than ideal to have different data structures from MNE. I'm still thinking about improvements.