Abstract
In this paper, an attempt has been made to explore the effect of incidental emotion on decision making. For this, first of all, a conventional emotion classification system based on electroencephalogram (EEG) signal is implemented. This emotion classification system ensures the generation and presence of four basic emotions, happy, relaxing, sad and angry by stimulations. audio-visual stimulations are used to generate specific emotions. The novelty of this work is in analysis of pre- and post-decisions taken with respect to stimulation provided. For this, answers of same question were taken before and after the induced emotions by stimulation. After observation and analysis, maximum (46.67%) percentage of change in decision has been noticed during angry emotion.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ahirwal, M.K., Kose, M.R.: Emotion recognition system based on EEG signal: a comparative study of different features and classifiers. In: Proceedings of Second IEEE Conference on Computing Methodologies and Communication (ICCMC), pp. 472–476 (2018)
Mishra, A., Bhateja, V., Gupta, A., Mishra, A., Satapathy, S.C.: Feature fusion and classification of EEG/EOG signals. In: Proceedings of Soft Computing and Signal Processing. Advances in Intelligent Systems and Computing (2019). (In press)
Majumdar, K.: Human scalp EEG processing: various soft computing approaches. Appl. Soft Comput. 11(8), 4433–4447 (2011)
Ahirwal, M.K., Kose, M.R.: Audio-visual stimulation based emotion classification by correlated EEG channels. Health Technol. (2019). (In press). https://doi.org/10.1007/s12553-019-00394-5
Ahirwal, M.K., Kumar, A., Londhe, N.D., Bikrol, H.: Scalp connectivity networks for analysis of EEG signal during emotional stimulation. In: Proceedings of IEEE Conference on Communication and Signal Processing (ICCSP), 0592-0596 (2016)
Chengtao, J., Natasha, M., Maurits, Roerdink, J.B.T.M.: Data-driven visualization of multichannel EEG coherence networks based on community structure analysis. Appl. Netw. Sci. 3(41), 1–24 (2018)
Van Kleef, G.A., De Dreu, C.K., Manstead, A.S.: An interpersonal approach to emotion in social decision making: the emotions as social information model. Adv. Exp. Soc. Psychol. 42, 45–96 (2010)
Andrade, E.B., Dan, A.: The enduring impact of transient emotions on decision making. Organ. Behav. Hum. Decis. Process. 109(1), 1–8 (2009)
Harlé, K.M., Sanfey, A.G.: Incidental sadness biases social economic decisions in the ultimatum game. Emotion 7(4), 876–881 (2007)
Acknowledgements
This research and study are done under a project entitled “Development of Computational Model for Decision Making based on Emotion Recognition through EEG signal” in file no. ECR/2017/000250, funded by SCIENCE & ENGINEERING RESEARCH BOARD (SERB) a statutory body of the Department of Science & Technology, government of India.
Declaration
We have taken permission from competent authorities to use the images/data as given in the paper. In case of any dispute in the future, we shall be wholly responsible.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ahirwal, M.K., Kose, M.R. (2021). Development of Emotional Decision-Making Model Using EEG Signals. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_27
Download citation
DOI: https://doi.org/10.1007/978-981-15-5788-0_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5787-3
Online ISBN: 978-981-15-5788-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)