Abstract
There has been a lot of attempts on estimating human emotions using physio-logical data, and it is expected to be applied to medical diagnosis. Recently, there is emotion estimation model using EEG and heart rate variability index-es as feature values, and applying deep learning to classify emotions with an accuracy of 61%. However, the accuracy may not be sufficient for applications such as medical diagnosis. In this study, we extracted and selected features of EEG and heart rate variability indexes in order to improve the accuracy. As a result, by using our proposed method to extract and select features, the accuracy of the model was increased to almost 100%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wang, X., Niea, D., Lu, B.: Emotional state classification from EEG data using machine learning approach. Neurocomputing 129, 94–106 (2014)
Ikeda, Y., Horie, R., Sugaya, M.: Estimate emotion with biological information for robot interaction. Procedia Comput. Sci. 112, 1589–1600 (2017)
Savery, R., Weinberg, G.: A survey of robotics and emotion: classifications and models of emotional interaction (2020). http://arxiv.org/abs/2007.14838
Frantzidis, C.A., et al.: On the classification of emotional biosignals evoked while viewing affective pictures: an integrated data-mining-based approach for healthcare applications. IEEE Trans. Inf Technol. Biomed. 14, 309–318 (2010)
Panicker, S.S., et al.: A survey of machine learning techniques in physiology based mental stress detection systems. Biocybern. Biomed. Engineering 39, 444–469 (2019)
Russel, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161 (1980)
Katsigiannis, S., Ramzan, N.: DREAMER: a database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE J. Biomed. Health Inform. 22, 98–107 (2018)
Moscato, F., et al.: Continuous monitoring of cardiac rhythms in left ventricular assist device patients. Artif. Organs 38(3), 191–198 (2014)
Urabe, N., Sugaya, M.: A proposal for individual emotion classification method using EEG and heart rate data and deep learning. In: The 34th National Convention. Japanese Society for Artificial Intelligence (2020)
Alfred, C.-K., Chia, W.C., Chin, S.W.: A mobile driver safety system: analysis of single-channel EEG on drowsiness detection. In: International Conference on Computational Science and Technology (ICCST) (2014)
Humiyasu, H., Koji, Y., Isao, M.: Comparative analysis of enforcement and memory during learning with a simple electroencephalograph. In: Multimedia, Distributed, Cooperative and Mobile (DICOMO2013) Symposium (2013)
Gupta, V., Chopda, M.D., Pachori, R.B.: Cross-subject emotion recognition using flexible analytic wavelet transform from EEG signals. IEEE Sens. J. 19, 2266–2274 (2019)
Duan, R.-N., Zhu, J.-Y., Lu, B.-L.: Differential entropy feature for EEG-based emotion classification. In: 6th Annual International IEEE EMBS Conference on Neural Engineering, pp. 81–84 (2013)
NeuroSky enc. EEG Algorithms (2021). http://neurosky.com/biosensors/eeg-sensor/algorithms/
Shinji, M., et al.: Physiological measurement and data analysis know-how for product development and evaluation, -characteristics of physiological indicators, how to measure, experimental design, data interpretation, and evaluation methods. Edited by PIE Research Group. The Japan Society for the Promotion of Interpersonal Engineering (2017)
Appelhans, B.M., Luecken, L.J.: Heart rate variability as an index of regulated emotional responding. Am. Psychol. Assoc. 10, 229–240 (2006)
Tomoi, D., Wen, W., Yamakawa, H., Yamashita, A., Takakusaki, K., Asama, H.: Estimating drivers’ stress during car racing using factor analysis. http://www.robot.t.u-tokyo.ac.jp/~yamashita/paper/E/E276Final.pdf
The Japan Diabetes Society. Diabetes Clinic Guideline 2019, p. 168 (2019)
Wagner, J., Kim, J., Andre, E.: From physiological signals to emotions: implementing and comparing selected methods for feature extraction and classification. In: IEEE International Conference on Multimedia and Expo (2005)
Strobl, C., Boulesteix, A.L., Zeileis, A., Hothorn, T.: Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinform. 8, 25 (2007)
Eerola, T., Vuoskoski, J.K.: A comparison of the discrete and dimensional models of emotion in music. Psychol. Music 39, 18–49 (2011)
Kim, J., Andre, E.: Emotion recognition based on physiological changes in music listening. IEEE Trans. Pattern Anal. Mach. Intell. 30, 2067–2083 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Suzuki, K., Matsubara, R., Laohakangvalvit, T., Sugaya, M. (2021). Feature Comparison of Emotion Estimation by EEG and Heart Rate Variability Indices and Accuracy Evaluation by Machine Learning. In: Ayaz, H., Asgher, U., Paletta, L. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 259. Springer, Cham. https://doi.org/10.1007/978-3-030-80285-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-80285-1_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80284-4
Online ISBN: 978-3-030-80285-1
eBook Packages: EngineeringEngineering (R0)