Inter-subject correlation (ISC) for EEG data

This repository contains several functions for calculating inter-subject correlation for electroencephalography (EEG) data. The algorithm was described in details in the next article Correlated Components of Ongoing EEG Point to Emotionally Laden Attention – A Possible Marker of Engagement?

Getting started

ISC.py contains all necessary functions for calculating Inter-subject correlation (ISC). The function train_cca(data) takes a dictionary where keys are names of conditions and values are 3-D numpy arrays structured like (subjects, channels, samples). It returns a 2-D numpy array W with all spatial filters (which maximize correlation in the data) in descending order.

The function apply_cca(X, W, fs) takes the same X as for training, numpy array of spatial filters and frequency sampling. It returns ISC, ISC_persecond, ISC_bysubject values and scalp projections of spatial filters A.

You can run the example below:

X = dict(Sin=np.random.rand(10, 32, 512*60))
X['Sin'][:,24,:] = np.sin(np.linspace(-np.pi, np.pi, 512*60))
[W, ISC_overall] = train_cca(X)

isc_results = dict()
for cond_key, cond_values in X.items():
    isc_results[str(cond_key)] = dict(zip(['ISC', 'ISC_persecond', 'ISC_bysubject', 'A'], apply_cca(cond_values, W, 250)))

For the visualization you should just run visualization.py after spatial filters are applied.

Some features will be added soon:

1. Scalp projections of spatial filters.
2. Statistical tests.
3. Visualisation with stimuli.