Tipo
Artigos em Revista
Tipo de Documento
Artigo Completo
Título
Predicting Exact Valence and Arousal Values from EEG
Participantes na publicação
Filipe Galvão (Author)
Soraia M. Alarcão (Author)
LASIGE
Manuel J. Fonseca (Author)
Dep. Informática
LASIGE
Resumo
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.
Data de Submissão/Pedido
2021-03-29
Data de Aceitação
2021-05-11
Data de Publicação
2021-05-14
Instituição
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA
Suporte
Sensors
Identificadores da Publicação
ISSN - 1424-8220
Editora
MDPI AG
Identificadores do Documento
DOI -
https://doi.org/10.3390/s21103414
URL -
http://dx.doi.org/10.3390/s21103414
Distinções
Editor’s Choice Article
Identificadores de Qualidade
Google Metrics (2021) - 104 - Engineering & Computer Science (general)
SCOPUS Q1 (2019) - 5 - Information Systems
Keywords
arousal and valence prediction
EEG
emotion recognition
comparative study