Abstract
This paper addresses the cerebral cortex maps construction from EEG signals getting an information simplification method for an emotional state phenomenon description. Bi-dimensional density distribution of main signal features are identified and a comparison to a previous approach is presented. Feature extraction scheme is performed via windowed EEG signals Stationary Wavelet Transform with the Daubechies Family (1–10); nine temporal and spectral descriptors are computed from the decomposed signal. Recursive feature selection method based on training a Random forest classifier using a one-vs-all scheme with the full features space, then a ranking procedure via gini importance, eliminating the bottom features and restarting the entire process over the new subset. Stopping criteria is the maximum accuracy. The main contribution is the analysis of the resulting subset features as a proxy for cerebral cortex maps looking for the cognitive processes understanding from surface signals. Identifying the common location of different emotional states in the central and frontal lobes, allowing to be strong parietal and temporal lobes differentiators for different emotions.
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
Chaparro, V., Gomez, A., Salgado, A., Quintero, O.L., Lopez, N., Villa, L.F.: Emotion recognition from EEG and facial expressions: a multimodal approach. In: IEEE Engineering in Medicine and Biology Society (EMBS) (2018)
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 2015, pp. 63–69 (2015)
Gómez, A., Quintero, L., López, N., Castro, J.: An approach to emotion recognition in single-channel EEG signals: a mother child interaction. J. Phys.: Conf. Ser. 705(1), 012051 (2016)
Gómez, A., Quintero, L., López, N., Castro, J., Villa, L., Mejía, G.: An approach to emotion recognition in single-channel EEG signals using stationary wavelet transform. In: IFMBE Proceedings, Claib 2016, pp. 654–657 (2017)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1–3), 389–422 (2002)
Kragel, P.A., LaBar, K.S.: Decoding the nature of emotion in the brain. Trends Cogn. Sci. 20, 1–12 (2016)
Menze, B.H., Kelm, B.M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., Hamprecht, F.A.: A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinform. 10(1), 213 (2009)
Restrepo, D., Gomez, A.: Short research advanced project: development of strategies for automatic facial feature extraction and emotion recognition. In: 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC), pp. 1–6. IEEE, October 2017
Scherer, R., Moitzi, G., Daly, I., Muller-Putz, G.R.: On the use of games for noninvasive EEG-based functional brain mapping. IEEE Trans. Comput. Intell. AI Games 5(2), 155–163 (2013)
Tracy, J.L., Randles, D.: Four models of basic emotions: a review of Ekman and Cordaro, Izard, Levenson, and Panksepp and Watt. Emot. Rev. 3(4), 397–405 (2011)
Uribe, A., Gomez, A., Bastidas, M., Quintero, O.L., Campo, D.: A novel emotion recognition technique from voiced-speech. In: 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC), pp. 1–4. IEEE, October 2017
Zheng, W.: Multichannel EEG-based emotion recognition via group sparse canonical correlation analysis. IEEE Trans. Cogn. Dev. Syst. 9(3), 281–290 (2017)
Acknowledgment
The authors would like to thank to the APOLO for the support to this project. This work has been partially funded by Colciencias Young researcher 2017 grant.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gómez, A., Quintero, O.L., Lopez-Celani, N., Villa, L.F. (2020). Cerebral Cortex Atlas of Emotional States Through EEG Processing. In: González Díaz, C., et al. VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering. CLAIB 2019. IFMBE Proceedings, vol 75. Springer, Cham. https://doi.org/10.1007/978-3-030-30648-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-30648-9_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30647-2
Online ISBN: 978-3-030-30648-9
eBook Packages: EngineeringEngineering (R0)