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Publication details

Document type
Journal articles

Document subtype
Full paper

Title
Predicting Exact Valence and Arousal Values from EEG

Participants in the publication
Filipe Galvão (Author)
Soraia M. Alarcão (Author)
LASIGE - LASIGE
Manuel J. Fonseca (Author)
Dep. Informática
LASIGE - LASIGE

Scope
International

Refereeing
Yes

Summary
Recognition of emotions from physiological signals, and in particular from electroencephalography (EEG), is a field within affective computing gaining increasing relevance. Although researchers have used these signals to recognize emotions, most of them only identify a limited set of emotional states (e.g., happiness, sadness, anger, etc.) and have not attempted to predict exact values for valence and arousal, which would provide a wider range of emotional states. This paper describes our proposed model for predicting the exact values of valence and arousal in a subject-independent scenario. To create it, we studied the best features, brain waves, and machine learning models that are currently in use for emotion classification. This systematic analysis revealed that the best prediction model uses a KNN regressor (K = 1) with Manhattan distance, features from the alpha, beta and gamma bands, and the differential asymmetry from the alpha band. Results, using the DEAP, AMIGOS and DREAMER datasets, show that our model can predict valence and arousal values with a low error (MAE < 0.06, RMSE < 0.16) and a strong correlation between predicted and expected values (PCC > 0.80), and can identify four emotional classes with an accuracy of 84.4%. The findings of this work show that the features, brain waves and machine learning models, typically used in emotion classification tasks, can be used in more challenging situations, such as the prediction of exact values for valence and arousal.

Date of Submisson/Request
2021-03-29
Date of Acceptance
2021-05-11
Date of Publication
2021-05-14

Institution
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA

Where published
Sensors

Publication Identifiers
ISSN - 1424-8220

Publisher
MDPI AG

Volume
21
Number
10

Starting page
3414

Document Identifiers
DOI - https://doi.org/10.3390/s21103414
URL - http://dx.doi.org/10.3390/s21103414

Rankings
Google Metrics (2021) - 104 - Engineering & Computer Science (general)
SCOPUS Q1 (2019) - 5 - Information Systems

Keywords
arousal and valence prediction EEG emotion recognition comparative study


Export

APA
Filipe Galvão, Soraia M. Alarcão, Manuel J. Fonseca, (2021). Predicting Exact Valence and Arousal Values from EEG. Sensors, 21, ISSN 1424-8220. eISSN . http://dx.doi.org/10.3390/s21103414

IEEE
Filipe Galvão, Soraia M. Alarcão, Manuel J. Fonseca, "Predicting Exact Valence and Arousal Values from EEG" in Sensors, vol. 21, 2021. 10.3390/s21103414

BIBTEX
@article{51209, author = {Filipe Galvão and Soraia M. Alarcão and Manuel J. Fonseca}, title = {Predicting Exact Valence and Arousal Values from EEG}, journal = {Sensors}, year = 2021, volume = 21 }