Affordable Access

deepdyve-link
Publisher Website

An Empirical Evaluation of Methodologies Used for Emotion Recognition via EEG Signals.

Authors
  • Hinvest, Neal S1
  • Ashwin, Chris1, 2
  • Carter, Felix1
  • Hook, James3
  • Smith, Laura G E1
  • Stothart, George1
  • 1 Department of Psychology, University of Bath, Bath, UK.
  • 2 Department of Psychology, Centre for Applied Autism Research (CAAR), University of Bath, Bath, UK.
  • 3 Department of Mathematical Sciences, University of Bath, Bath, UK.
Type
Published Article
Journal
Social Neuroscience
Publisher
Informa UK (Taylor & Francis)
Publication Date
Feb 01, 2022
Volume
17
Issue
1
Pages
1–12
Identifiers
DOI: 10.1080/17470919.2022.2029558
PMID: 35045797
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

A goal of brain-computer-interface (BCI) research is to accurately classify participants' emotional status via objective measurements. While there has been a growth in EEG-BCI literature tackling this issue, there exist methodological limitations that undermine its ability to reach conclusions. These include both the nature of the stimuli used to induce emotions and the steps used to process and analyze the data. To highlight and overcome these limitations we appraised whether previous literature using commonly used, widely available, datasets is purportedly classifying between emotions based on emotion-related signals of interest and/or non-emotional artifacts. Subsequently, we propose new methods based on empirically driven, scientifically rigorous, foundations. We close by providing guidance to any researcher involved or wanting to work within this dynamic research field.

Report this publication

Statistics

Seen <100 times