Abstract
Affective computing has become one of the emerging technologies in the current arena as most of the industries depend on the consumers and their feedbacks. Opinion and emotional feedbacks are playing major ways to improve the quality of services provided by the industry. Affective computing plays a vital role in analyzing emotional feedbacks. The emotions of the human being can be derived by using various ways including facial emotions and textual emotions. Recent research uses biological signals to detect the emotions of human beings. The emotions include anger, sadness, happiness, joy, disgust, surprise generating promising parameters with the biological signal from EEG. Electroencephalography (EEG) based on unique subject identification is evaluated using the presented system. Biological signals are prone to motion artifacts inside the body that distracts during the recordings. EEG signals are complex with numerous oscillating points that are unique in certain cases. The proposed system focused on capturing the impacted component in the brain wave data that produce the unique identification of subjects. The states of the brain wave data like alpha, beta, gamma, theta, and delta are keenly monitored with the help of frequency-domain analysis through discrete wavelet transforms (DWT). In this paper, the analysis of subject impacted factors is detected using a novel multi-nominal regression-based (UCI) unique covariate identity detection algorithm. The proposed system also compared with state-of-the-art approaches in terms of accuracy, precision, and error rate.
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
References
Zhang T, Zheng W, Cui Z, Zong Y, Li Y (2019) Spatial-temporal recurrent neural network for emotion recognition. IEEE Trans Cybern 49(3):839–847. https://doi.org/10.1109/TCYB.2017.2788081
Wu D, Zhang J, Zhao Q (2020) Multimodal fused emotion recognition about expression-EEG interaction and collaboration using deep learning. IEEE Access 8:133180–133189. https://doi.org/10.1109/ACCESS.2020.3010311
Ji N et al (2019) EEG signals feature extraction based on DWT and EMD combined with approximate entropy. Brain Sci 9(8):201. https://doi.org/10.3390/brainsci9080201
Bakkialakshmi VS, Thalavaipillai S (2022) AMIGOS: a robust emotion detection framework through Gaussian ResiNet. Bull Electr Eng Inform 11(4):2142–2150. https://doi.org/10.11591/eei.v11i4.3783
Gannouni S, Aledaily A, Belwafi K, Aboalsamh H (2021) Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification. Sci Rep 11(1)
Islam KA, Tcheslavski GV (2015) Independent component analysis for EOG artifacts minimization of EEG signals using kurtosis as a threshold. In: 2015 2nd international conference on electrical information and communication technologies (EICT), pp 137–142. https://doi.org/10.1109/EICT.2015.7391935
Bakkialakshmi VS, Sudalaimuthu T (2022) Dynamic cat-boost enabled keystroke analysis for user stress level detection. In: International conference on computational intelligence and sustainable engineering solutions (CISES), Greater Noida, India, pp. 556–560. https://doi.org/10.1109/CISES54857.2022.9844331
Subasi A, Ismail Gursoy M (2010) EEG signal classification using PCA, ICA, LDA, and support vector machines. Expert Syst Appl 37(12):8659–8666
Bhardwaj A, Gupta A, Jain P, Rani A, Yadav J (2015) Classification of human emotions from EEG signals using SVM and LDA classifiers. In: 2015 2nd international conference on signal processing and integrated networks (SPIN), pp 180–185. https://doi.org/10.1109/SPIN.2015.7095376
Wang M, Abdelfattah S, Moustafa N, Hu J (2018) Deep Gaussian mixture-hidden Markov model for classification of EEG signals. IEEE Trans Emerg Top Comput Intell 2(4):278–287. https://doi.org/10.1109/TETCI.2018.2829981
Jirayucharoensak S, Pan-Ngum S, Israsena P (2014) EEG-based emotion recognition using deep learning network with principal component-based covariate shift adaptation. Sci World J 2014:1–10
Hulliyah K et al (2021) Analysis of emotion recognition model using electroencephalogram (EEG) signals based on stimuli text. Turk J Comput Math Educ (TURCOMAT) 12(3):1384–1393
Bakkialakshmi VS, Sudalaimuthu T (2021) A survey on affective computing for psychological emotion recognition. In: 2021 5th international conference on electrical, electronics, communication, computer technologies, and optimization techniques (ICEECCOT). IEEE, pp 480–486. https://doi.org/10.1109/ICEECCOT52851.2021.9707947
Sudalaimuthu T, Bakkialakshmi VS (2021) Emo-Gem: an impacted affective emotional psychology analysis through Gaussian model using AMIGOS. J Positive Sch Psychol 6(3):6417–6424 (ISSN 2717-7564)
Koelstra S et al (2011) DEAP: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput 3(1):18–31
Arnau-Gonzalez P, Arevalillo-Herraez M, Katsigiannis S, Ramzan N (2021) On the influence of effect in EEG-based subject identification. IEEE Trans Affect Comput 12(2):391–401
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bakkialakshmi, V.S., Sudalaimuthu, T. (2024). Unique Covariate Identity (UCI) Detection for Emotion Recognition Through EEG Signals. In: Borah, M.D., Laiphrakpam, D.S., Auluck, N., Balas, V.E. (eds) Big Data, Machine Learning, and Applications. BigDML 2021. Lecture Notes in Electrical Engineering, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-99-3481-2_56
Download citation
DOI: https://doi.org/10.1007/978-981-99-3481-2_56
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3480-5
Online ISBN: 978-981-99-3481-2
eBook Packages: Computer ScienceComputer Science (R0)