Abstract
The Subliminal Affective Priming Effect (SAPE) can be accomplished on the arousal level, which manifests that the effect appears when the subjects judge the arousal values of the target stimulus. Based on Electroencephalogram (EEG) signals, this paper analyzes whether the SAPE appears on the arousal level. For effectively solving the problems of the modal aliasing and reconstruction error for EEG study, we introduce the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm. And Multi-scale Permutation Entropy (MPE) is a common method for the time series. According to the nonlinear characteristics of EEG signals, this paper combines CEEMDAN with MPE to the judgement of SAPE. Firstly, this paper introduces the principle and computational procedure of the analysis method of SAPE. Then, we perform a one-way repeated-measures ANOVAs (ORANOVAs) for the arousal values. At last, the experimental results are analyzed. The experimental results show that the SAPE occurs in the negative priming group, but SAPE is not observed in the positive priming group, which demonstrates that the analysis method can be used to judge whether the SAPE exists on the arousal level. We also further verify this conclusion by performing the ORANOVAs.
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Liu, R.H., Wang, L.: Subliminal affective priming effect. Psychol. Sci. 23, 97–110 (2000). https://doi.org/10.3969/j.issn.1671-6981.2000.03.024
Luo, Y.J., Wu, J.H.: Emotional psychological control and cognitive research strategies. J. Southwest Norm. Univ. 31, 26–29 (2005). https://doi.org/10.3969/j.issn.1673-9841.2005.02.005
Luo, Y.J., Huang, Y.X., Li, X.Y., et al.: Effects of emotion on cognitive processing: Series of event-related potentials study. Adv. Psychol. Sci. 14, 505–510 (2006). https://doi.org/10.3969/j.issn.1671-3710.2006.04.005
Nie, D., Wang, X.W., Duan, R.N., et al.: A survey on EEG based on emotion recognition. Chin. J. Biomed. Eng. 31, 595–606 (2012). https://doi.org/10.3969/j.issn.0258-8021.2012.04.018
Huang, N.E., Shen, Z., Long, S.R., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. A 454, 903–995 (1998). https://doi.org/10.1098/rspa.1998.0193
Bajaj, V., Pachori, R.B.: Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans. Inf Technol. Biomed. 16, 1135–1142 (2012). https://doi.org/10.1109/titb.2011.2181403
Gao, J., Deng, J.W.: Empirical mode decomposition and analysis of its evaluation criteria. Sci. Technol. Rev. 33, 108–112 (2015). https://doi.org/10.3981/j.issn.1000-7857.2015.02.016
Wu, Z.: Ensemble empirical mode decomposition: a noise assisted data analysis method. Adv. Adapt. Data Anal. 01, 1–41 (2009). https://doi.org/10.1142/S1793536909000047
Torres, M.E., Colominas, M.A., Schlotthauer, G., et al.: A complete ensemble empirical mode decomposition with adaptive noise. In: Acoustics: 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 4144–4147 (2011). https://doi.org/10.1109/icassp.2011.5947265
Han, Q.Y., Sun, Q., Wang, X.D., et al.: Application of CEEMDAN in Raman spectroscopy denoising. Laser Optoelectron. Prog. 52, 274–280 (2015). https://doi.org/10.3788/lop52.113003
Zhan, L.W., Li, C.W.: A comparative study of empirical mode decomposition-based filtering for impact signal. Entropy 19, 13–26 (2016). https://doi.org/10.3390/e19010013
Zhu, M., Duan, Z.S., Guo, B.L., et al.: Application of CEEMDAN combined with LMS algorithm in signal de-noise of bearings. Noise Vib. Control 38, 144–149 (2018). https://doi.org/10.3969/j.issn.1006-1355.2018.02.028
Li, F., Lin, Y., Zhao, H., et al.: Fault diagnosis of hydraulic pump based on CEEMDAN-SVM. Hydraul. Pneum. 01, 125–129 (2016). https://doi.org/10.11832/j.issn.1000-4858.2016.01.026
Nicolaou, N., Georgiou, J.: Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst. Appl. 39, 202–209 (2012). https://doi.org/10.1016/j.eswa.2011.07.008
Aziz, W., Arif, M.: Multiscale permutation entropy of physiological time series. In: 2005 Pakistan Section Multitopic Conference 2005, pp. 10–23 (2005). https://doi.org/10.1109/inmic.2005.334494
Sun, X., Zou, Y., Nikiforova, V., et al.: The complexity of gene expression dynamics revealed by permutation entropy. BMC Bioinform. 11, 607 (2010). https://doi.org/10.1186/1471-2105-11-607
Liu, X.F., Hu, B., Zheng, X.W.: Facial expression awareness based on multiscale permutation entropy of EEG. Int. J. Data Min. Inform. 21, 287–300 (2018). https://doi.org/10.1504/ijdmb.2018.098936
Li, T.T., Lu, Y.: The subliminal affective priming effects of faces displaying various levels of arousal: an ERP study. Neurosci. Lett. 583, 148–153 (2014). https://doi.org/10.1016/j.neulet.2014.09.027
Wang, X.W., Shi, L.C., Lu, B.L.: A survey on the technology of dry electrodes for EEG recording. Chin. J. Biomed. Eng. 29, 777–784 (2010). https://doi.org/10.3969/j.issn.0258-8021.2010.05.022
Acknowledgements
National Natural Science Foundation of China (61373149) and the Taishan Scholars Program of Shandong Province, China.
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Zhang, M., Hu, B., Zhang, Y., Zheng, X. (2019). An Analysis Method for Subliminal Affective Priming Effect Based on CEEMDAN and MPE. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_25
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DOI: https://doi.org/10.1007/978-981-15-1377-0_25
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