EmoRecap

This repository contains code used for data processing and analysis for:

Bowen, H. J., Fields, E. C., & Kensinger, E. A. (2019). Prior Emotional Context Modulates Early Event-Related Potentials to Neutral Retrieval Cues. Journal of Cognitive Neuroscience, 31(11), 1755-1767. https://doi.org/10.1162/jocn_a_01451

Usage/Workflow

Preprocessing

1. Run EmoRecap_preprocess. This will ask for a subject ID, but can also be run as a batch by giving a file with each subject ID on a different line or by supplying a cell array of subject IDs at the top of the file. This script imports the data, adds channel location information, references the data, applies a high pass filter, and bins and epochs the data. (Note: Various parameters used in preprocessing can be found in EmoRecap_preproc_params.m)

Artifact rejection and correction

2. Run pre_ICA_rej and supply subject ID.
3. Scroll through epochs and mark any with significant non-ocular or muscular artifact by clicking on them.
4. When done, click UPDATE MARKS.
5. Run save_ICA_rej, which will save the marked epochs so that they are not used in the next ICA step.
6. Run EmoRecap_run_ICA. This will automatically run ICA for any subjects for whom the above pre-ICA rejection has been done but who do not yet have an ICA weight matrix. This script will run ICA and save the weight matrix in the ICA folder.
7. After ICA is done, run from_preart and supply a subject ID. This will load the subject's data and create (or load, if already created) a script for applying ICA correction and detecting and rejecting trials with artifact remaining after ICA correction.
8. Examine ICA components and determine which to remove. Specify these in the ICrej variable in the arf script.
9. Run the arf script and examine the data. If rejection does not look satisfactory, answer no to saving the data, adjust parameters, and re-run.
10. Once satisfied, save the data. You will then be prompted if you want to calculate ERPs.

ERP manipulations and grand mean

• Additional bins and difference waves are added to all subject ERPsets with EmoRecap_add_ERP_bins
• A grand mean ERPset can be created with EmoRecap_make_gm.

Statistical Analysis

Statistical analysis makes use of the Factorial Mass Univariate Toolbox (FMUT):
https://github.com/ericcfields/FMUT/wiki

1. Analysis was conducted on 10 Hz low pass filtered ERPsets produced with the batch_filter_ERP function.
2. EmoRecap_make_GND creates the GND structures used by FMUT.
3. EmoRecap_mass_uni_analysis runs the stats.

Figures and visualization

• Some useful code for creating figures can be found in the stats/figures folder.