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Electroencephalography (EEG) is a powerful non-invasive brain imagingtechnique with a high temporal resolution that has seen extensive use acrossmultiple areas of cognitive science research. This thesis adaptsrepresentational similarity analysis (RSA) to single-trial EEG datasets andintroduces its principles to EEG researchers unfamiliar with multivariateanalyses. We have two separate aims: 1. we want to explore the effectiveness ofsingle-trial RSA on EEG datasets; 2. we want to utilize single-trial RSA andcomputational semantic models to investigate the role of semantic meaning inemotional word processing. We report two primary findings: 1. single-trial RSAon EEG datasets can produce meaningful and interpretable results given a highnumber of trials and subjects; 2. single-trial RSA reveals that emotionalprocessing in the 500-800ms time window is associated with additional semanticanalysis.

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