Abstract
The development of a suitable EEG-based emotion recognition system has become a target in the last decades for BCI (Brain Computer Interface) applications. However, there are scarce algorithms and procedures for real time classification of emotions. In this work we introduce a new approach to select the appropriate parameters in order to build up a real-time emotion recognition system. We recorded the EEG-neural activity of 5 participants while they were looking and listening to an audiovisual database composed by positive and negative emotional video clips. We tested 11 different temporal window sizes, 6 ranges of frequency bands and 5 areas of interest located mainly on prefrontal and frontal brain regions. The most accurate time window segment was selected for each participant, giving us probable positive and negative emotional characteristic patterns, in terms of the most informative frequency-location pairs. Our preliminary results provide a reliable way to establish the more appropriate parameters to develop an accurate EEG-based emotion classifier in real-time.
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
Ekman, P.: Facial expression and emotion. Am. Psychol. 48(4), 384 (1993)
Appelhans, B.M., Luecken, L.J.: Heart rate variability as an index of regulated emotional responding. Rev. Gen. Psychol. 10(3), 229–240 (2006). doi:10.1037/1089-2680.10.3.229
Khalfa, S., Isabelle, P., Jean-Pierre, B., Manon, R.: Event-related skin conductance responses to musical emotions in humans. Neurosci. Lett. 328(2), 145–149 (2002). doi:10.1016/S0304-3940(02)00462-7
Mauss, I.B., Robinson, M.D.: Measures of emotion: a review. Cogn. Emot. 23(2), 209–237 (2009). doi:10.1080/02699930802204677. http://www.tandfonline.com/doi/abs/10.1080/02699930802204677
Jatupaiboon, N., Pan-Ngum, S., Israsena, P., Chen, B.-W., Hsieh, S., Wu, C.-H.: Real-time EEG-based happiness detection system. Sci. World J. 2013 (2013). doi:10.1155/2013/618649. http://dx.doi.org/10.1155/2013/618649
Uhrig, M.K., Trautmann, N., Baumgärtner, U., Treede, R.-D., Henrich, F., Hiller, W., Marschall, S.: Emotion elicitation: a comparison of pictures and films. Front. Psychol. 7, 180 (2016). doi:10.3389/fpsyg.2016.00180. http://dx.doi.org/10.3389/fpsyg.2016.00180
Soleymani, M., Member, S., Lee, J.-S.: DEAP: a database for emotion analysis using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)
Carvalho, S., Leite, J., Galdo-Álvarez, S., Gonçalves, Ó.F.: The emotional movie database (EMDB): a self-report and psychophysiological study. Appl. Psychophysiol. Biofeedback 37(4), 279–294 (2012). doi:10.1007/s10484-012-9201-6
Liu, Y.-J., Yu, M., Zhao, G., Song, J., Ge, Y., Shi, Y.: Real-time movie-induced discrete emotion recognition from EEG signals. IEEE Trans. Affect. Comput. 3045(c), 1 (2017). doi:10.1109/TAFFC.2017.2660485. http://dx.doi.org/10.1109/TAFFC.2017.2660485
Candra, H., Yuwono, M., Chai, R., Handojoseno, A., Elamvazuthi, I., Nguyen, H.T., Su, S.: Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2015, pp. 7250–7253, November 2015. doi:10.1109/EMBC.2015.7320065
Russell, J.: A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178 (1980). doi:10.1037/h0077714
Davidson, R.J., Ekman, P., Saron, C.D., Senulis, J.A., Friesen, W.V.: Approach-withdrawal and cerebral asymmetry: emotional expression and brain physiology: I. J. Pers. Soc. Psychol. 58, 330–341 (1990). doi:10.1037/0022-3514.58.2.330
Oldfield, R.C.: The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9(1), 97–113 (1971)
Klem, G.H., Lüders, H.O., Jasper, H.H., Elger, C.: The ten-twenty electrode system of the international federation. Electroencephalogr. Clin. Neurophysiol. 52(Suppl.), 3–6 (1999)
Ferree, T.C., Luu, P., Russell, G.S., Tucker, D.M.: Scalp electrode impedance, infection risk, and EEG data quality. Clin. Neurophysiol. 112(3), 536–544 (2001)
Jung, T.-P., Humphries, C., Lee, T.-W., Makeig, S., McKeown, M.J., Iragui, V., Sejnowski, T.J.: Extended ICA removes artifacts from electroencephalographic recordings. Adv. Neural Inf. Process. Syst. 10, 894–900 (1998)
Delorme, A., Makeig, S.: EEGLAB: an open sorce toolbox for analysis of single-trail EEG dynamics including independent component anlaysis. J. Neurosci. Methods 134, 9–21 (2004). doi:10.1016/j.jneumeth.2003.10.009
Acknowledgement
This work has been supported in part by the Spanish National Research Program (MAT2015-69967-C3-1), by a research grant of the Spanish Blind Organization (ONCE), by the Ministry of Education of Spain (FPU grant AP-2013/01842) and by Séneca Foundation - Agency of Science and Technology of the Region of Murcia.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Sorinas, J. et al. (2017). Setting the Parameters for an Accurate EEG (Electroencephalography)-Based Emotion Recognition System. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-59740-9_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59739-3
Online ISBN: 978-3-319-59740-9
eBook Packages: Computer ScienceComputer Science (R0)