8000 GitHub - dawnfinzi/Retrocue6: EEG ICA process technique (applied for Retrocue 6 experiment)
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Retrocue6

This directory contains the analysis scripts accompanying the manuscript: Finzi, Wagner, Itthipuripat & Aron, 2017, Stopping cognition: unexpected events recruit prefrontal inhibitory control and interrupt working memory (in prep)

Analysis and scripts adapted from: Wagner, J., Wessel, J., Gharemahni, A. & Aron, A.R. 2017, Establishing a right frontal beta signature for stopping action in scalp EEG: implications for testing inhibitory control in other task contexts.

For more details on data and the paradigm please refer to the manuscript (Finzi, Wagner, Itthipuripat & Aron, in prep) when available or contact me at: rebecca.d.finzi@gmail.com. Additional information and the behavioral/EEG data is available on OSF.

Dependencies

This analysis pipeline requires eeglab (version 13_6_5b) which can be downloaded from the EEGLAB website.

Scripts/analysis stream

Overall

  1. preproc.m
    • preprocessing of stop data
    • preprocessing of WM data
    • merging of the two datasets
  2. add_error_codes.m
    • add response error behavioral data to EEG data
  3. run_ica.m
    • run ica on the merged datasets
  4. post_ICA.m
    • apply weights to EEG data
    • compute dipoles
  5. brain_comp_select.m
    • select the ICs that appear to reflect brain activity
  6. create_study.m
    • select only the brain components (determined in #5)
    • epoch datasets
    • create eeglab STUDY
    • precompute component measures for clustering
  7. clustering.m
    • cluster components
    • save right frontal beta cluster

Right frontal beta cluster

  1. computeERSP.m
    • compute event related spectral perturbations for right frontal beta clustering
  2. AV_ERSP.m
    • average cluster ERSP images for each condition, compute significance and plot differences
  3. computeERSP_st.m
    • compute single trial ERSPs for each subject in right frontal beta cluster
  4. regression.m (calls regressERSP_Z.m)
    • run regression for each subject testing effect of power in time frequency space on behavioral response error
    • compute significance on group level for standard and novel conditions using standardized beta coefficients
    • plot significant results for each condition
    • for novel condition, pool across subjects and plot power (at peak) response error for visualization purposes

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EEG ICA process technique (applied for Retrocue 6 experiment)

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