Abstract
This research case focused on the development of an emotion classification system aimed to be integrated in projects committed to improve assistive technologies. An experimental protocol was designed to acquire an electroencephalogram (EEG) signal that translated a certain emotional state. To trigger this stimulus, a set of clips were retrieved from an extensive database of pre-labeled videos. Then, the signals were properly processed, in order to extract valuable features and patterns to train the machine and deep learning models. There were suggested 3 hypotheses for classification: recognition of 6 core emotions; distinguishing between 2 different emotions and recognising if the individual was being directly stimulated or merely processing the emotion. Results showed that the first classification task was a challenging one, because of sample size limitation. Nevertheless, good results were achieved in the second and third case scenarios (70% and 97% accuracy scores, respectively) through the application of a recurrent neural network.
Chapter PDF
Similar content being viewed by others
References
IntellWheels2.0 – Intelligent Wheelchair with Flexible Multimodal Interface and Realistic Simulator. Optimizer, Lda, FEUP, UA, Rehapoint, GroundControl. Available at http://www.intellwheels.com/en/client/skins/geral.php?id=25 Cited 24 May 2021
Sono ao Volante 2.0 - Information system for predicting sleeping while driving and detecting disorders or chronic sleep deprivation. Optimizer, Lda, FEUP, IS, IPCA. Available at http://sonoaovolante.com/en/client/skins/geral.php?id=25 Cited 24 May 2021
Lobes of the Brain. UQ-Queensland Brain Institute (2018). Available at https://qbi.uq.edu.au/brain/brain-anatomy/lobes-brain.Cited26May2021
Eckman, P.: Facial Expressions of Emotion: New Findings, New Questions In: Psychological Science, 34–38. Sage Journals (1992)
Harmon-Jones, C., Bastian, B., Harmon-Jones, E.: The Discrete Emotions Questionnaire: A New Tool for Measuring State Self-Reported Emotions. In: PLoS One 11(8), e0159915 (2016) https://doi.org/10.1371/journal.pone.0159915.
Cowen, A., Keltner, D.: Self-report captures 27 distinct categories of emotion bridged by continuous gradients In: Proceedings of the National Academy of Sciences of the United States of America 14(38), E7900-E7909 (2017) https://doi.org/10.1073/pnas.1702247114.
López-Gil, J.-M., Virgili-Gomá, J., Gil, R., Guilera, T., Batalla, I., Soler-González, J., García, R.: Method for Improving EEG Based Emotion Recognition by Combining It with Synchronized Biometric and Eye Tracking Technologies in a Non-invasive and Low Cost Way. In: Frontiers in Computational Neuroscience 10, 85 (2016) https://doi.org/10.3389/fncom.2016.00119
Jenke, R., Peer, A., Buss, M.: Feature Extraction and Selection for Emotion Recognition from EEG. In: IEEE Transactions on Affective Computing, 5(3), 327–339, (2014) https://doi.org/10.1109/TAFFC.2014.2339834
Richman, J. S., Moorman, J. R.: Physiological time-series analysis approximate entropy and sample entropy. In: American Journal of Physiology-Heart and Circulatory Physiology (2000) https://doi.org/10.1152/ajpheart.2000.278.6.H2039
Junsheng, C., Dejie, Y., Yu, Y.: Research on the intrinsic mode function (IMF) criterion in EMD method In: Mechanical Systems and Signal Processing, 20(4), 817–824. (2006) https://doi.org/10.1016/j.ymssp.2005.09.011
Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. In: Bioinformatics Computation Biol., 3(2) 185–205, (2003) https://doi.org/10.1142/S0219720005001004
Zain, M. A.: Predicting Emotions Using EEG Data with Recurrent Neural Networks. Geek Culture (2021) Available at https://medium.com/geekculture/predicting-emotions-using-eeg-data-with-recurrent-neural-networks-8acf384896f5 Cited 19 May 2021
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2023 The Author(s)
About this paper
Cite this paper
Rodrigues, D., Reis, L.P., Faria, B.M. (2023). Emotion Classification Based on Single Electrode Brain Data: Applications for Assistive Technology. In: Brito, P., Dias, J.G., Lausen, B., Montanari, A., Nugent, R. (eds) Classification and Data Science in the Digital Age. IFCS 2022. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-031-09034-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-031-09034-9_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09033-2
Online ISBN: 978-3-031-09034-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)