N400-P600 GAT Decoding of semantic anomalies using EEG

Data is accessible from https://zenodo.org/record/1185944#.Wye6JC-Q1TY

preprocessing-ICA-Autoreject:

script for preprocessing the recorded subject vhdr files, correcting for ICA and auto-rejection and repair

• creates raw files, ICA files and the autorejection file for subjects.

make_epochs:

script for epoching the data

• takes the original events and identifies the onset of the sentence and recreates the events file so that triggers index target word onsets.
• epochs the data into -300 to 1300 ms relative to target word onset
• applies ICA correction
• saves epoched data

ERPs:

script for ERP analysis

• plots butterfly plot (Incongruent minus Congruent Trials), averaged ROIs (left/right, anterior/posterior) ERPs for congruent and incongruent trials with 95% bootstrapped variance corridors and topographies for Incongruent minus Congruent trials.
• creates a massive dataframe of epoch data over subjects
• creates and saves two separate csvs for N400 and P600 for 2x2x2 anova (congruent/incongruent, anterior/posterior, left/right)

MVPA:

Script for MVPA analysis:

• creates machine learning pipeline (works for Ridge Classifier, Logistic Regression, linear and non-linear (RBF) SVM).
• investigates how well patterns from 300-500ms (N400) and 600-800ms (P600) generalize over time.

Plot the MVPA results in a figure with 4 subplots:

• plots the GAT matrix masked by significance (Threshold Free Cluster Enhancement - p<0.01)
• plots diaogonal decoding performance (trained and tested at same time points - masked for significance p<0.05 [thin lines] and p<0.01 [thick lines], FDR corrected)
• plots component generalization (N400 and P600 classifier generalization over time - masked for significance p<0.05 [thin lines] and p<0.01 [thick lines], FDR corrected)
• plots difference between classifier performances (P600-N400 decoding accuracy) to identify time points where one classifier performed better than the other (below 0% for N400 better than P600 and above 0% P600 better than N400) and masked for significance (p<0.05 [thin lines] and p<0.01 [thick lines], FDR corrected)

Control for subject level decoding performance:

• sort individual subject decoders for respective time windows of interest
• conduct a regression analysis predicting, at each time point, the decoding strength (outcome variable) based on the rank order of decoding strength averaged over the training window (predictor variable).
• plot heat maps of sorted, subject level, decoding performance for each time window of interest and the regression coefficients (with 95% confidence intervals).

additional-MVPA-analysis

Script for additional analysis to control for inter-trial variance and P600 time window selection

• control for inter-trial variance by sorting individual subjects trials by prediction strengths (classifier confidence scores) in time windows of interest. Plots simulations for stable vs. latency varying signal and data.

• control for whether conclusions arise from specific time window of interest for P600, test component generalization with P600 defined as 500-700, 600-800, 700-900, 800-1000 and 600-1000ms.