Emotions Recognition Using EEG Signals: A Survey

Soraia M. Alarcão · Manuel J. Fonseca

1,199 citations0/0 files storedIEEE Transactions on Affective Computing

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Emotion and Mood Recognition EEG and Brain-Computer Interfaces Gaze Tracking and Assistive Technology

Abstract

Emotions have an important role in daily life, not only in human interaction, but also in decision-making processes, and in the perception of the world around us. Due to the recent interest shown by the research community in establishing emotional interactions between humans and computers, the identification of the emotional state of the former became a need. This can be achieved through multiple measures, such as subjective self-reports, autonomic and neurophysiological measurements. In the last years, Electroencephalography (EEG) received considerable attention from researchers, since it can provide a simple, cheap, portable, and ease-to-use solution for identifying emotions. In this paper, we present a survey of the neurophysiological research performed from 2009 to 2016, providing a comprehensive overview of the existing works in emotion recognition using EEG signals. We focus our analysis in the main aspects involved in the recognition process (e.g., subjects, features extracted, classifiers), and compare the works per them. From this analysis, we propose a set of good practice recommendations that researchers must follow to achieve reproducible, replicable, well-validated and high-quality results. We intend this survey to be useful for the research community working on emotion recognition through EEG signals, and in particular for those entering this field of research, since it offers a structured starting point.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 1,199 citations exist upstream.

References

1994Measuring emotion: The self-assessment manikin and the semantic differential 1997Affective Computing 2005International affective picture system (IAPS) : affective ratings of pictures and instruction manual 2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals 2009Measures of emotion: A review 2011A Multimodal Database for Affect Recognition and Implicit Tagging 2013Differential entropy feature for EEG-based emotion classification 2006Discovering Psychology

Cited by

2020EEG-Based Emotion Recognition Using Regularized Graph Neural Networks 2022A systematic review on affective computing: emotion models, databases, and recent advances 2020EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention 2022EEG Based Emotion Recognition: A Tutorial and Review 2020EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM 2022TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.