Abstract
Research in cognitive neuroscience has found emotion-induced distinct cognitive variances between the left and right hemispheres of the brain. In this work, we follow up on this idea by using Phase-Locking Value (PLV) to investigate the EEG based hemispherical brain connections for emotion recognition task. Here, PLV features are extracted for two scenarios: Within-hemisphere and Cross-hemisphere, which are further selected using maximum relevance-minimum redundancy (mRmR) and chi-square test mechanisms. By making use of machine learning (ML) classifiers, we have evaluated the results for dimensional model of emotions through making binary classification on valence, arousal and dominance scales, across four frequency bands (theta, alpha, beta and gamma). We achieved the highest accuracies for gamma band when assessed with mRmR feature selection. KNN classifier is most effective among other ML classifiers at this task, and achieves the best accuracy of 79.4%, 79.6%, and 79.1% in case of cross-hemisphere PLVs for valence, arousal, and dominance respectively. Additionally, we find that cross-hemispherical connections are better at predictions on emotion recognition than within-hemispherical ones, albeit only slightly.
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
Cao, J., et al.: Brain functional and effective connectivity based on electroencephalography recordings: a review. Hum. Brain Mapp. 43(2), 860–879 (2021). https://doi.org/10.1002/hbm.25683
Cao, R., et al.: EEG functional connectivity underlying emotional valance and arousal using minimum spanning trees. Front. Neurosci. 14, 355 (2020). https://doi.org/10.3389/fnins.2020.00355
Chavan, A., Kolte, M.: Improved EEG signal processing with wavelet based multiscale PCA algorithm. In: 2015 International Conference on Industrial Instrumentation and Control (ICIC), pp. 1056–1059 (2015). https://doi.org/10.1109/IIC.2015.7150902
Chen, M., Han, J., Guo, L., Wang, J., Patras, I.: Identifying valence and arousal levels via connectivity between EEG channels. In: 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 63–69 (2015). https://doi.org/10.1109/ACII.2015.7344552
Dimond, S.J., Farrington, L., Johnson, P.: Differing emotional response from right and left hemispheres. Nature 261(5562), 690–692 (1976). https://doi.org/10.1038/261690a0
Pearson, K.: X. on the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Lond. Edinburgh Dublin Phil. Maga. J. Sci. 50(302), 157–175 (1900). https://doi.org/10.1080/14786440009463897
Koelstra, S., et al.: Deap: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012). https://doi.org/10.1109/T-AFFC.2011.15
Kumari, N., Anwar, S., Bhattacharjee, V.: Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet. Neural Comput. Appl. 34(16), 13291–13303 (2022). https://doi.org/10.1007/s00521-022-06942-x
Lachaux, J.P., Rodriguez, E., Martinerie, J., Varela, F.J.: Measuring phase synchrony in brain signals. Hum. Brain Mapp. 8(4), 194–208 (1999). https://doi.org/10.1002/(sici)1097-0193(1999)8:4<194::aid-hbm4>3.0.co;2-c
Li, X., Hu, B., Sun, S., Cai, H.: EEG-based mild depressive detection using feature selection methods and classifiers. Comput. Methods Programs Biomed. 136, 151–161 (2016). https://doi.org/10.1016/j.cmpb.2016.08.010
Li, Y., Zheng, W., Zong, Y., Cui, Z., Zhang, T., Zhou, X.: A bi-hemisphere domain adversarial neural network model for EEG emotion recognition. IEEE Trans. Affect. Comput. 12(2), 494–504 (2021). https://doi.org/10.1109/taffc.2018.2885474
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005). https://doi.org/10.1109/TPAMI.2005.159
Singh, M.K., Singh, M.: A deep learning approach for subject-dependent and subject-independent emotion recognition using brain signals with dimensional emotion model. Biomed. Signal Process. Control 84, 104928 (2023). https://doi.org/10.1016/j.bspc.2023.104928
Wang, Y., Huang, Z., McCane, B., Neo, P.: Emotionet: a 3-d convolutional neural network for EEG-based emotion recognition. In: 2018 IJCNN, pp. 1–7 (2018). https://doi.org/10.1109/IJCNN.2018.8489715
Wang, Z., Tong, Y., Heng, X.: Phase-locking value based graph convolutional neural networks for emotion recognition. IEEE Access 7, 93711–93722 (2019). https://doi.org/10.1109/access.2019.2927768
Zhang, J., Zhang, X., Chen, G., Huang, L., Sun, Y.: EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres. Front. Neurosci. 16, 974673 (2022). https://doi.org/10.3389/fnins.2022.974673
Zheng, W.L., Lu, B.L.: Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans. Auton. Ment. Dev. 7(3), 162–175 (2015). https://doi.org/10.1109/tamd.2015.2431497
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 Switzerland AG
About this paper
Cite this paper
Ruchilekha, Srivastava, V., Singh, M.K. (2024). Emotion Recognition Using Phase-Locking-Value Based Functional Brain Connections Within-Hemisphere and Cross-Hemisphere. In: Choi, B.J., Singh, D., Tiwary, U.S., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2023. Lecture Notes in Computer Science, vol 14531. Springer, Cham. https://doi.org/10.1007/978-3-031-53827-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-53827-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53826-1
Online ISBN: 978-3-031-53827-8
eBook Packages: Computer ScienceComputer Science (R0)