Survey of Affect Representation Schemes in Physiological Automatic Affect Recognition

Survey of Affect Representation Schemes in Physiological Automatic Affect Recognition: A Systematic Literature Review

Pietnoczko, Natalia (TU Delft Electrical Engineering, Mathematics and Computer Science)

Raman, C.A. (mentor)
Dudzik, B.J.W. (mentor)
Liem, C.C.S. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-28

Physiological signals, such as Electroencephalogram (EEG), Glavic Skin Response (GSR), or Body Temperature, are common inputs for Automatic Affect Recognition (AAR) systems. One of the crucial elements of AAR is the Affect Representation Scheme (ARS) used to define the affective states recognized by the system (e.g., happiness, sadness, fear, anger). Throughout the years many AAR reviews have been published. However, most of them do not include a detailed analysis of ARSs and the motivation behind them. This paper aims to fill this knowledge gap by performing a Systematic Literature Review (SLR) of Computer Science papers that propose a Physiological-signal-based AAR (PAAR) system. We explore how researchers discuss and choose an ARS and whether they base it on actual psychological theories. Eligible papers are retrieved from 4 databases: Web Of Science, IEEExplore, Scopus, and ACM Digital Library. Due to time limitations, the review is done rapidly and some additional search constraints are applied for feasibility. The most significant restrictions are: considering papers published since 2020 and performing experiments on specific benchmarking datasets. We take these constraints and their possible impact into consideration when interpreting the results. The presented review procedure can be stripped from the additional filters and reused for a full review. In total 115 papers are processed. The majority of papers introduce an EEG-based emotion recognition system. The analysis of the extracted information reveals that dimensional ARSs, in particular, Valence/Arousal model is the most popular. Moreover, authors often choose to reduce the dimensions to high/low values. Categorical ARSs are less frequent and usually are adopted from the dataset. Lastly, the authors do not provide extensive motivation for the choice of ARS and rarely refer to psychological theories.

Physiological Signals
Affective Computing
Emotion Representation
Affect Representation Scheme

http://resolver.tudelft.nl/uuid:f71097d4-670b-42a1-9d37-43d3f92b5663

Student theses

bachelor thesis

© 2023 Natalia Pietnoczko