Inter Subject Correlation in EEG

This is matlab code to compute intersubject correlation (ISC) in EEG using correlated component analysis,
as described in the references below (description closest to the present code is in Cohen and Parra, 2016).

isceeg.m -- EEG specific code, keeping clutter to a minimum.

Download the follwing zip file, uncompress and execute isceeg in matlab: isc.zip (429 MB)

These are the specific files that are included in this zip file:

topoplot.m -- stand-alone version of EEGlab's popular display function (does not require EEGlab).
BioSemi64.loc -- BioSemi location file for topoplot
notBoxPlot.m -- stand-alone version of Rob Campbell's scatter plot (does not require suplementary functions)
EEGVolume.mat -- Samantha Cohen's EEG data with 64 electrodes from 18 subjects while watching this video. (Here's a list of all stimuli).

Plus some other useful code:

preprocessEEG_RPCA.m -- EEG preprocessing routine utilizing robust PCA (RPCA).
multiStimISC.m -- ISC routine that can be run on multiple stimuli simultaneously
Channel selection GUI -- Really nice tool select bad channels, which is an important input to the ISC code if you want nice results.

Stimuli:

Here are all of the audiovisual (AV), audiovisual scrambled (AVsc), and audio only (A Only) videos used in Cohen and Parra (2016)
(The visual only (V Only) stimuli from this paper are identical to the audiovisual stimuli except they are played silently.)

Faster code:
More efficient code to perform Correlated Component Analysis (the core of the analysis method we have used to measures ISC in EEG) is now available here:

Correlated Component Analysis code

References:
Find papers here.

1. Lucas C. Parra, Stefan Haufe, Jacek P. Dmochowski, "Correlated Components Analysis --- Extracting Reliable Dimensions in Multivariate Data", Neurons, Behavior, Data Analysis and Theory (NBDT). arXiv:1801.08881. Jan 29, 2019. paper
2. Samantha S Cohen*, Jens Madsen*, Gad Touchan, Denise Robles, Stella F. A. Lima, Simon Henin, Lucas C. Parra, "Neural engagement with online educational videos predicts learning performance for individual students", Neurobiology of learning and memory. 2018 Nov 1;155:60-4. paywalled paper same as preprint
3. Agustin Petroni*, Samantha S. Cohen*, Lei Ai, Nicolas Langer, Simon Henin, Tamara Vanderwal, Michael P Milham, and Lucas C Parra, The Variability of Neural Responses to Naturalistic Videos Change with Age and Sex, eNeuro, January 2018. Full text
4. Ivan Iotzov*, Brian C Fidali*, Agustin Petroni, Mary M Conte, Nicholas D Schiff, Lucas C Parra, Divergent neural responses to narrative speech in disorders of consciousness, Annals of Clinical and Translational Neurology:2328-9503, July 20, 2017. Full text
5. Samantha S. Cohen, Simon Henin, Lucas C. Parra, Engaging narratives evoke similar neural activity and lead to similar time perception, Scientific Reports, 6:4578, July 2017. Full text
6. Andreas Trier Poulsen, Simon Kamronn, Jacek Dmochowski, Lucas C. Parra, Lars Kai Hansen, "Measuring engagement in a classroom: Synchronised neural recordings during a video presentation." Scientific Reports, 7, February, 2017. PDF
7. Samantha S. Cohen, Lucas C. Parra, Memorable audiovisual narratives synchronize sensory and supramodal neural responses, eNeuro, 3(6), November 2016. Full text
8. Jason Ki, Simon Kelly, Lucas C. Parra, Attention strongly modulates reliability of neural responses to naturalistic narrative stimuli. Journal of Neuroscience, 36 (10), 3092-3101. PDF
9. Jacek P. Dmochowski, Matthew A. Bezdek, Brian P. Abelson, John S. Johnson, Eric H. Schumacher, Lucas C. Parra, Audience preferences are predicted by temporal reliability of neural processing, Nature Communication, 5567, July 2014.
10. Jacek P. Dmochowski, Paul Sajda, Joao Dias, Lucas C. Parra, Components of ongoing EEG with high correlation point to emotionally-laden attention -- a possible marker of engagement?, Frontiers in Human Neuroscience, 6:112, April 2012.