On-line recognition of emotions via electroencephalography



Emotion Recognition, Brain-Computer Interface, Electroencephalography, On-line Processing


Automated pattern recognition of brain signals can bring about new experiences, enhancing applications in a wide array of areas. One of its fields of study is the recognition of emotions via electroencephalography (EEG), which shows exclusive advantages compared to other methods. However, research with brain-computer interfaces (BCI) is usually structured in two sequential stages: data collection and data analysis. These stages leave a gap in the perspective of a functional system in a production environment since the practitioner needs to wait a considerable length of time until they can see the results of the current activity. An on-line classification system of emotions (positive, neutral, and negative) was developed using open resources in this work. Five machine learning models were trained with the SEED IV dataset, which is labeled with different emotions. The models were trained and tested using nested cross-validation and grid search to obtain the best hyperparameters. The algorithm implementation in Python was integrated with the OpenBCI software to capture the EEG signals, process them, and command the simulations. The best average accuracy obtained for a single subject was 76.19%, and the average accuracy for all subjects was 57.07%. The average execution time for signal processing and prediction, combined, was around one millisecond, which demonstrates the potential for applications with real-time characteristics.


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Author Biographies

Kevin Altamirano Asher Weiss, University of Vale do Itajaí

Kevin was born in São Paulo, Brazil, in 1995. In 2020, he received his bachelor degree in Computer Engineering from the Universidade do Vale de Itajaí, Brazil. Currently, he works as a Software Engineer developing Web-based Enterprise Applications. His research interests include microcontrollers, hardware and software integration, artificial intelligence, and the internet of things.

Fernando Concatto, Universidade do Vale do Itajaí

Fernando graduated in Computer Science with honors at Universidade do Vale do Itajaí, Brazil, in 2020, and is currently a student in the Master's Program in Applied Computing, at the same university. He has been actively participating in various research projects since 2016, in the fields of social network analysis, mathematical optimization and brain-computer interfaces. At the moment, he works as a lead software developer and is a Fapesc research fellow, investigating the rehabilitation of neurological disorders.

Raimundo Celeste Ghizoni Teive, University of Vale do Itajaí

Raimundo graduated at Federal University of Santa Catarina (UFSC), Brazil, in Electrical Engineering in 1985. He earned his M.S. and PhD. degrees both at UFSC, in 1991 and 1997, respectively. In 1994, he obtained a split PhD at Queen Mary University of London. He is lecturer in the Master's Program in Applied Computing at Universidade do Vale de Itajaí. His research interest areas include application of Artificial Intelligence techniques into engineering problems. He has over one hundred published papers. He is a Elsevier and IEEE reviewer.

Alejandro Rafael Garcia Ramirez, University of Vale do Itajaí

Alejandro received the Electronics Engineer degree from the Instituto Superior Politécnico José Antonio Echeverría, Cuba, in 1989, and the Dr. of Electrical Engineering degree from the Universidade do Estado de Santa Catarina, Brasil, in 2003. He is currently Associate Professor at Universidade do Vale do Itajaí and Researcher at Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). His research interests include Embedded Systems, Robotics and Assistive Technologies.


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How to Cite

Altamirano Asher Weiss, K., Concatto, F., Celeste Ghizoni Teive, R., & Garcia Ramirez, A. R. (2022). On-line recognition of emotions via electroencephalography. IEEE Latin America Transactions, 20(5), 806–812. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6063

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