Using Single-Trial Representational Similarity Analysis with EEG to track semantic similarity in emotional word processing
This work addresses EEG researchers by introducing a novel analysis method for cognitive science, but it is incremental as it adapts existing RSA techniques to a new application.
The study adapted representational similarity analysis (RSA) to single-trial EEG data to track semantic similarity in emotional word processing, finding that it yields interpretable results with sufficient trials and subjects and that emotional processing involves additional semantic analysis in the 500-800ms window.
Electroencephalography (EEG) is a powerful non-invasive brain imaging technique with a high temporal resolution that has seen extensive use across multiple areas of cognitive science research. This thesis adapts representational similarity analysis (RSA) to single-trial EEG datasets and introduces its principles to EEG researchers unfamiliar with multivariate analyses. We have two separate aims: 1. we want to explore the effectiveness of single-trial RSA on EEG datasets; 2. we want to utilize single-trial RSA and computational semantic models to investigate the role of semantic meaning in emotional word processing. We report two primary findings: 1. single-trial RSA on EEG datasets can produce meaningful and interpretable results given a high number of trials and subjects; 2. single-trial RSA reveals that emotional processing in the 500-800ms time window is associated with additional semantic analysis.