  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.
