EEG IMAGING APPLICATION ON POSITIVE EMOTION OF AFFECTIVE NEUROSCIENCE
Abstract
This research paper explored the brain electrical activities of human positive emotions through Electroencephalography (EEG) device. Modern day EEG device has made it possible to record and analyze the electrophysiological pattern of the brainwaves. Brain reactions to certain situation and environment affect human physical body and produce physiological or emotions effects. However, the correlation of human emotion and its electrophysiological is still poorly understood. Although many scientific research has been carried out, it is still remain a big challenge as the physiology and psychology of human and living being cannot be quantify directly. The previous study investigated the positive emotion of the affective neuroscience. Despite being important for many functioning in humans, it is far less researched compared to negative emotions. This study elaborates its findings by investigating the relation between positive emotion with EEG frequency and its cognitive relation to the brain lobes. Human subjects participated for this experiment and were assigned to watch several stimulus videos while wearing the Emotiv Epoch to record their brainwaves. The results show that different positive emotions generate different spatial patterns. The spatial patterns and frequency bands for joy, inspiration and serenity particularly shows distinguishable difference compared to other positive emotions.
Downloads
References
[2] J. Posner, J.A. Russell and B.S. Peterson, “The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development and psychopathology,” Development and Psychopathology, vol. 17, no. 3, pp. 715-734, 2005.
[3] S.M. Alarcao, and M.J. Fonseca, “Emotions recognition using EEG signals: A survey”, IEEE Transactions on Affective Computing, vol. 10, no. 3, pp. 374-393, 2017.
[4] X. Hu, J. Yu, M. Song, C. Yu, F. Wang, P. Sun, D. Wang and D. Zhang, “EEG correlates of ten positive emotions”, Frontiers in Human Neuroscience, vol. 11, no. 26, pp. 1-12, 2017.
[5] F. C. Kao, S. P. Wang and Y. J. Chang, “Brainwaves analysis of positive and negative emotions”, Information Science and Applications, vol. 12, pp. 1263-1266, 2015.
[6] M. Weymar and L. Schwabe, “Amygdala and emotion: The bright side of it”, Frontiers in Neuroscience, vol. 10, no. 224, pp. 1-3, 2016.
[7] iMotions Biometric Research Platform. (2016). EEG: Pocket Guide [Online]. Available: https://imotions.com/blog/eeg/
[8] J. Kagan, “Brain and emotion”, Emotion Review, vol. 10, no. 1, pp. 79-86, 2018.
[9] L. Bonnet, A. Comte, L. Tatu, J-L. Millot, T. Moulin and E. Medeiros de Bustos, “The role of the amygdala in the perception of positive emotions: an “intensity detector””, Frontiers in Behavioral Neuroscience, vol. 9, no. 178, pp. 1-12, 2015.
[10] W.W. Ismail, M. Hanif, S.B. Mohamed, N. Hamzah and Z.I. Rizman, “Human emotion detection via brain waves study by using electroencephalogram (EEG)”, International Journal on Advanced Science Engineering Information Technology, vol. 6, no. 6, pp. 1005-1011, 2016.
[11] A. Hossan and A.M. Chowdhury, “Real time EEG based automatic brainwave regulation by music,” in 5th International Conference on Informatics, Electronics and Vision, Dhaka, Bangladesh, 2016, pp. 780-784.
[12] P. Ekkekakis, The measurement of affect, mood and emotion: A guide for health-behavioral research. Cambridge, UK: Cambridge University Press, 2013.
[13] Y-Y. Lee and S. Hsieh, “Classifying Different Emotional States by Means of EEG-Based Functional Connectivity Patterns”, PLOS One, vol. 9, no. 4, pp. 1-13, 2014.
[14] I.B. Mauss and M.D. Robinson, “Measures of emotion: A review”, Cognition & Emotion, vol. 23, no. 2, pp. 209-237, 2009.
[15] J. Burgdorf and J. Panksepp, “The neurobiology of positive emotions”, Neuroscience and Biobehavioural Reviews, vol. 30, no.2, pp. 209-237, 2006.
[16] L. Machado and A. Cantilino, “A systematic review of the neural correlates of positive emotions”, Brazilian Journal of Psychiatry, vol. 39, no. 2, pp. 172-179, 2017.
[17] S. Ocklenburg, J. Peterburs, J. Mertzen, J. Schmitz, O. Gunturkun and G. Grimshaw, “Effects of emotional valence on hemispheric asymmetries in response inhibition”, Symmetry, vol. 9, no. 8, pp. 1-13, 2017.
[18] EMOTIV EPOC. (2014). Brain computer interface & scientific contextual EEG [Online]. Available: https://www.emotiv.com/files/Emotiv-EPOC-Product-Sheet-2014.pdf
[19] King of Clickbait. (2018). Oppo mascot dance funny (turn down for what) [Online].Available: https://www.youtube.com/watch?v=gjnGbApmVPg
[20] Olympic. (2016). Neymar penalty seals gold for hosts, Rio 2016 Olympic Games [Online]. Available: https://www.youtube.com/watch?v=-Jmjn_dJt_4
[21] René Marie. (2015). Beach Cottage Life, One Minute of Serenity, Green Waves Blue Sea Style [Online]. Available: https://www.youtube.com/watch?v=
bsZ17Ya1Zlc
[22] MovieScenes - Clips. (2017). Photo frame scares the Wyatt Wolf Scene, IT [Online]. Available: https://www.youtube.com/watch?v=aRyct02kXuc
[23] K. Nesbitt, K. Blackmore, G. Hookham, F. Kay-Lambkin and P. Walla, “Using the startle eye-blink to measure affect in players,” in Serious Games Analytics: Advances in Game-Based Learning, C. Loh, Y. Sheng and D. Ifenthaler, Eds. Switzerland: Springer, 2015, pp. 401-434.
[24] Z. Mahmoodin, W. Mansor, K.Y. Lee and N.B. Mohamad, “An analysis of EEG signal power spectrum density generated during writing in children with dyslexia”, in 11th International Colloquium on Signal Processing & Its Applications, Kuala Lumpur, 2015, pp. 156-160.
[25] J. Sick, E. Bray, A. Bregy, W.D. Dietrich, H.M. Bramlett and T. Sick, “EEGgui: a program used to detect electroencephalogram anomalies after traumatic brain injury”, Source Code for Biology and Medicine, vol. 8, no. 12, pp. 1-7, 2013.
