A package to analyse MNE-formatted EEG data for steady-state visually evoked potentials (SSVEPs).
pip install git+https://github.com/janfreyberg/ssvepy.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 http://www.janfreyberg.com/ssvepy. 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: https://github.com/janfreyberg/ssvepy/blob/master/example.ipynb
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
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_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=NoneThe powerspectrum. Needs to be a numpy array with dimensions: (epochs, channels, frequency)
freqs=NoneThe 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:
intermodulations: non-linear combination of your input stimulus frequencies, all with the attributes:
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
hdf5file that can be loaded with
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.