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

arXiv: https://arxiv.org/abs/2110.03529 · HTML: https://mlsanity.com/paper/2110.03529/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 46 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Interpreting Encoding and Decoding Models](https://mlsanity.com/paper/1812.00278/) (2018)
- [Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data](https://mlsanity.com/paper/1606.02840/) (2016)

## Abstract (arXiv)

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.