A Method for Quantifying the Emotional Intensity and Duration of a Startle Reaction with Customized Fractal Dimensions of EEG Signals
- 1 School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, Australia
Abstract
The assessment of emotions with fractal dimensions of EEG signals has been attempted before, but the quantification of the intensity and duration of sudden and short emotions remains a challenge. This paper suggests a method for this purpose, by using a new fractal dimension algorithm and by adjusting the amplitude of the EEG signal in order to obtain maximal separation of high and low fractal dimensions. The emotion was induced by embedding a scary image at 20 seconds in landscape videos of 60 seconds length. The new method did not only detect the onset of the emotion correctly, but also revealed its duration and intensity. The intensity is based on the magnitude and impulse of the fractal dimension signal. It is also shown that Higuchi’s method does not always detect emotion spikes correctly; on the contrary, the region of the expected emotional response can be represented by fractal dimensions smaller than the rest of the signal, whereas the new method directly reveals distinct spikes. The duration of these spikes was 10 - 11 seconds. The magnitude of these spikes varied across the EEG channels. The build-up and cool-down of the emotions can occur with steep and flat gradients.
- Sourina, O. and Liu, Y. (2011) A Fractal-Based Algorithm of Emotion Recognition from EEG Using Arousal-Valence Model. Proceedings of Biosignals 2011, Rome, 26-29 January 2011, 209-214.
- Liu, Y. and Sourina, O. (2014) EEG-Based Subject-Dependent Emotion Recognition Algorithm Using Fractal Dimension. Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics, San Diego, 5-8 October 2014, 3166-3171. http://dx.doi.org/10.1109/SMC.2014.6974415
- Higuchi, T. (1988) Approach to an Irregular Time Series on the Basis of the Fractal Theory. Physica D, 31, 277-283. http://dx.doi.org/10.1016/0167-2789(88)90081-4
- Kulish, V.V., Sourin, A.I. and Sourina, O. (2007) Fractal Spectra and Visualization of the Brain Activity Evoked by Olfactory Stimuli. Proceedings of the 9th Asian Symposium on Visualization, Hong Kong, 4-9 June 2007, 371-378.
- Tripathy, J., Fuss, F.K., Kulish, V.V. and Yang, S. (2008) The Influence of Hues on the Cortical Activity—A Recipe for Selecting Sportswear Colours. In: Fuss, F.K., Subic, A. and Ujihashi, S., Eds., The Impact of Technology on Sport II, Taylor and Francis Group, London, 141-148.
- Kulish, V., Sourin, A. and Olga Sourina, O. (2006) Human Electroencephalograms Seen as Fractal Time Series: Mathematical Analysis and Visualization. Computers in Biology and Medicine, 36, 291-302. http://dx.doi.org/10.1016/j.compbiomed.2004.12.003
- Koelstra, S., Mühl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi T., Pun, T., Nijholt, A. and Patras, I. (2012) DEAP: A Database for Emotion Analysis Using Physiological Signals. IEEE Transactions on Affective Computing, 3, 18-31. http://dx.doi.org/10.1109/T-AFFC.2011.15
- Ekman, P., Friesen, W.V. and Simons, R.C. (1985) Is the Startle Reaction an Emotion? Journal of Personality and Social Psychology, 49, 1416-1426. http://dx.doi.org/10.1037/0022-3514.49.5.1416
- Fuss, F.K. (2013) A Robust Algorithm for Optimisation and Customisation of Fractal Dimensions of Time Series Modified by Nonlinearly Scaling Their Time Derivatives: Mathematical Theory and Practical Applications. Computational and Mathematical Methods in Medicine, 2013, Article ID: 178476. http://dx.doi.org/10.1155/2013/178476
- Tan, A.M., Fuss, F.K., Weizman, Y. and Azari, M.F. (2015) Centre of Pressure Detection and Analysis with a High-Resolution and Low-Cost Smart Insole. Procedia Engineering, 112, 146-151. http://dx.doi.org/10.1016/j.proeng.2015.07.190