Using Single-Trial Representational Similarity Analysis with EEG to track semantic similarity in emotional word processing

TL;DR

This paper adapts single-trial representational similarity analysis (RSA) to EEG data to investigate how semantic meaning influences emotional word processing, revealing meaningful neural patterns in specific time windows.

Contribution

It introduces the application of single-trial RSA to EEG data and demonstrates its effectiveness in studying semantic processing in emotional word recognition.

Findings

Single-trial RSA produces interpretable results with sufficient trials and subjects.

Emotional processing in 500-800ms is linked to additional semantic analysis.

Abstract

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.