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Emotional state detection based on common spatial patterns of EEG

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Abstract

The application of EEG-based emotional states is one of the most vital phases in the context of neural response decoding. Emotional response mostly appears in the presence of visual, auditory, tactile, and gustatory arousals. In our work, we use visual stimuli to evaluate the emotional feedback. One of the best performing methods in emotion estimation applications is the common spatial patterns (CSP). We implement CSP method in addition to the conventional Welch power spectral density-based analysis. Experimental results and topographies on the collected EEG data show that the CSP spatial filtering method implies the relationship between EEG bands, EEG channels, neural efficiency and emotional stimuli types.

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References

  1. Wu, D., King, J.T., Chuang, C.H., Lin, C.T., Jung, T.P.: Spatial filtering for EEG based regression problems in brain–computer interface (BCI). IEEE Trans. Fuzzy Syst. 26(2), 771 (2018)

    Article  Google Scholar 

  2. Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., Muller, K.R.: Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process. Mag. 25, 41 (2008)

    Article  Google Scholar 

  3. Zheng, W.L., Zhu, J.Y., Lu, B.L.: Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans. Affect. Comput. pp 1–1 (2017)

  4. Russell, J.: A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161 (1980)

    Article  Google Scholar 

  5. Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): affective ratings of pictures and instruction manual. The Center for Research in Psychophysiology, University of Florida A-8 (2008)

  6. Cuthbert, B.N., Schupp, H.T., Bradley, M.M., Birbaumer, N., Lang, P.J.: Brain potentials in affective picture processing: covariation with autonomic arousal and affective report. Biol. Psychol. 52(2), 95 (2000)

    Article  Google Scholar 

  7. Lang, P.J.: A bio-informational theory of emotional imagery. Psychophysiology 16(6), 495–512 (1979)

    Article  Google Scholar 

  8. Dan-Glauser, E.S., Scherer, K.R.: The Geneva affective picture database (GAPED): a new 730-picture database focusing on valence and normative significance. Behav. Res. Methods 43(2), 468 (2011)

    Article  Google Scholar 

  9. Ahern, G.L., Schwartz, G.E.: Differential lateralization for positive and negative emotion in the human brain: EEG spectral analysis. Neuropsychologia 23(6), 745 (1985)

    Article  Google Scholar 

  10. Ekman, P.: An argument for basic emotions. Cognit. Emot. 6(3–4), 169 (1992)

    Article  Google Scholar 

  11. Ekman, P., Rolls, E., Perrett, D., Ellis, H.: Facial expressions of emotion: an old controversy and new findings. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 335(1273), 63 (1992)

    Article  Google Scholar 

  12. Lang, P.J., Greenwald, M.K., Bradley, M.M., Hamm, A.O.: Looking at pictures: affective, facial, visceral, and behavioral reactions. Psychophysiology 30(3), 261 (1993)

    Article  Google Scholar 

  13. Evans, F.J., Cook, M.R., Cohen, H.D., Orne, E., Orne, M.T.: Appetitive and replacement naps: EEG and behavior. Science 197, 687 (1977)

    Article  Google Scholar 

  14. Marchewka, A., Żurawski, Ł., Jednoróg, K., Grabowska, A.: The Nencki Affective Picture System (NAPS): introduction to a novel, standardized, wide-range, high-quality, realistic picture database. Behav. Res. Methods 46(2), 596 (2014)

    Article  Google Scholar 

  15. Bradley, M.M., Lang, P.J.: Measuring emotion: the self assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25(1), 49 (1994)

    Article  Google Scholar 

  16. Goodman, A.M., Katz, J.S., Dretsch, M.N.: Military Affective Picture System (MAPS): a new emotion-based stimuli set for assessing emotional processing in military populations. J. Behav. Ther. Exp. Psychiatry 50, 152 (2016)

    Article  Google Scholar 

  17. Migliore, S., Curcio, G., Porcaro, C., Cottone, C., Simonelli, I., D’aurizio, G., Landi, D., Palmieri, M., Ghazaryan, A., Squitieri, F., Filippi, M., Vernieri, F.: Emotional processing in RRMS patients: dissociation between behavioural and neurophysiological response. Mult. Scler. Relat. Disord. 27, 344 (2019)

    Article  Google Scholar 

  18. Stevens, E.M., Frank, D., Codispoti, M., Kypriotakis, G., Cinciripini, P.M., Claiborne, K., Deweese, M.M., Engelmann, J.M., Green, C.E., Karam-Hage, M., Minnix, J.A., Ng, J., Robinson, J.D., Tyndale, R.F., Vidrine, D.J., Versace, F.: The late positive potentials evoked by cigarette-related and emotional images show no gender differences in smokers. Sci. Rep. 9(1), 3240 (2019)

    Article  Google Scholar 

  19. Jenke, R., Peer, A., Buss, M.: Feature extraction and selection for emotion recognition from EEG. IEEE Trans. Affect. Comput. 5(3), 327 (2014)

    Article  Google Scholar 

  20. Lin, Y.P., Wang, C.H., Jung, T.P., Wu, T.L., Jeng, S.K., Duann, J.R., Chen, J.H.: EEG-based emotion recognition in music listening. IEEE Trans. Biomed. Eng. 57(7), 1798 (2010)

    Article  Google Scholar 

  21. Tibdewal, M.N., Fate, R.R., Mahadevappa, M., Ray, A.K., Malokar, M.: Classification of artifactual EEG signal and detection of multiple eye movement artifact zones using novel time-amplitude algorithm. SIViP 11(2), 333 (2017)

    Article  Google Scholar 

  22. Goshvarpour, A., Abbasi, A., Goshvarpour, A., Daneshvar, S.: Discrimination between different emotional states based on the chaotic behavior of galvanic skin responses. SIViP 11(7), 1347 (2017)

    Article  Google Scholar 

  23. Villar, A.J.: Comparative study of robust methods for motor imagery classification based on CSP and LDA. In: Latin American Congress on biomedical engineering CLAILAB, 2016, pp 126–129 (2017)

  24. Gao, L., Cheng, W., Zhang, J., Wang, J.: EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine. Rev. Sci. Instrum. 87(8), 085110 (2016)

    Article  Google Scholar 

  25. Özerdem, M.S., Polat, H.: Emotion recognition based on EEG features in movie clips with channel selection. Brain Inform. 4(4), 241 (2017)

    Article  Google Scholar 

  26. Jin, Y., Mousavi, M., de Sa, V.R.: 2018 Adaptive CSP with subspace alignment for subject to subject transfer in motor imagery brain computer interfaces. In: 6th International Conference on Brain–Computer Interface (BCI), pp 1–4 (2018)

  27. Al-dabag, M.L., Ozkurt, N.: EEG motor movement classification based on cross-correlation with effective channel. Signal Image Video Process. (2018)

  28. EEG channel placement. https://www.biosemi.com/headcap.htm. Accessed 14 Sept 2019

  29. Basar, M.D., Duru, A.D., Ozgor, S., Ozgor, C., Akan, A.: Analysis of reduced EEG channels based on emotional stimulus. In: 26th Signal Processing and Communications Applications Conference (SIU), pp. 1–4 (2018)

  30. Welch, P.: The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70 (1967)

    Article  Google Scholar 

  31. Koles, Z.: The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. Electroencephalogr. Clin. Neurophysiol. 79(6), 440 (1991)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Electrical and Electronics Engineering, Istanbul University-Cerrahpasa, Avcilar, Istanbul, Turkey

    Merve Dogruyol Basar

  2. Neuroscience in Sports Laboratory, Faculty of Sport Science, Marmara University, Beykoz, Istanbul, Turkey

    Adil Deniz Duru

  3. Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli, Izmir, Turkey

    Aydin Akan

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  1. Merve Dogruyol Basar
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  2. Adil Deniz Duru
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  3. Aydin Akan
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Correspondence to Merve Dogruyol Basar.

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Basar, M.D., Duru, A.D. & Akan, A. Emotional state detection based on common spatial patterns of EEG. SIViP 14, 473–481 (2020). https://doi.org/10.1007/s11760-019-01580-8

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  • DOI: https://doi.org/10.1007/s11760-019-01580-8

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