Loading Data into Unfold

Unfold is generally agnostic to how you load your data. You only require a Matrix (channel x time) or 3D-Array(channel x time x epochs) and an event-dataframe.

Setup

using Unfold
using UnfoldMakie,CairoMakie

using DataFrames

MNE Demo Dataset

The easiest way to showcase this is to simply use a demo-dataset from MNE.

To extract the metadata, we need Pandas which is not installed by default, we therefore need to install it via CondaPkg

using CondaPkg
CondaPkg.add("pandas")
CondaPkg.add("mne") # due to a bug in PyMNE https://github.com/beacon-biosignals/PyMNE.jl/issues/38 - we have to add mne additionally
using PyMNE

Now we are ready to load some data.

limo_epochs = PyMNE.datasets.limo.load_data(subject=1,path="~/MNE/DATA",update_path=false)
limo_epochs

After loading, we can fit an Unfold model to it.

First extract the data & convert it to Julia/Unfold requirements

data = pyconvert(Array,limo_epochs.get_data(picks="B11"))
data  = permutedims(data,[2,3,1]) # permute to ch x times x epochs Array format

events = DataFrame(PyTable(limo_epochs.metadata))
rename!(events,2 => :coherence)
events.face = string.(events.face)

times = pyconvert(Vector,limo_epochs.times)

Next fit an Unfold Model:

uf = fit(UnfoldModel,[Any=>(@formula(0~face+coherence),times)],events,data)
results = coeftable(uf)

plot_results(results)

Read some of your own data

We can make use of all PyMNE importer functions to load the data. Try it for your own data! Get starting with Unfold in no-time!

#eeglabdata = PyMNE.io.read_raw_eeglab("pathToEEGLabSet.set")

Some extra conversions are needed to import the data from PyMNE to Unfold (as shown above). We could try putting these in a wrapper function - do you want to tackle this challenge? Would be a great first contribution to the toolbox :-)

Loading Data into Unfold

Setup

MNE Demo Dataset

Read some of your own data

Contribute?