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Classification of Subliminal Affective Priming Effect Based on AE and SVM

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Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1042))

Abstract

The study of the Subliminal Affective Priming Effect (SAPE) mainly uses event-related potential technology and mapping method. Many researches are only for the study of emotional classification, but there are few researches on the classification of the SAPE. That is, the SAPE is directly judged by the psychologist in most experiment. So, this paper designs a classifier based on Automatic Encoder (AE) and Support Vector Machine (SVM) for automatic recognition of SPAE. Initially, this paper collects EEG signal, and then extracts statistical features from EEG signal to form a data set. After that, the data set is dimension reduction by AE and then divided into training set and test set randomly. At last, the already designed model is trained with the training set and validated with the test set. In the experiment, we find that the designed classifier has the best performance compared with the classifiers based on BP neural network, Principal Component Analysis (PCA) and SVM. The experimental results show that the average classification accuracy is 95.31%. The classification results further indicate that the SAPE’s judgment is hopeful to reduce the labor with the machine.

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References

  1. Nie, D., Wang, X.W., Duan, R.N., Lv, B.L.: A survey on EEG based on emotion recognition. J. Chin. J. Biomed. Eng. 31(4), 595–606 (2012). https://doi.org/10.3969/j.issn.0258-8021.2012.04.018

  2. Li, T.T., Lu, Y.: The subliminal affective priming effects of faces displaying various level of arousal: an ERP study. Neuroscience Lett. 583, 148–153 (2014). https://doi.org/10.1016/j.neulet.2014.09.027

    Article  Google Scholar 

  3. Yi, J.Y., Zhong, M.T., Luo, Y.Z., Ling, Y., Yao, S.Q.: A study of subliminal affective priming with affective pictures. Chin. J. Clin. Psychol. 15(3), 304 (2007). https://doi.org/10.16128/j.cnki.1005-3611.2007.03.030

    Article  Google Scholar 

  4. Takahashi, K.: Remarks on SVM-based emotion recognition from multi-modal bio-potential signals. In: 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No.04TH8759) (2004). https://doi.org/10.1109/roman.2004.1374736

  5. Liu, L., Chen, J., Xu, L.: Realization and application research of BP neural network based on MATLAB. J. Int. Seminar Future BioMed. Inform. Eng. 2008, 130–133 (2008). https://doi.org/10.1109/fbie.2008.92

    Article  Google Scholar 

  6. Jin, H.L., Zhang, Z.H.: Research of movement imagery EEG based on hilbert-huang transform and BP neural network. Chin. J. Biomed. Eng. 2013(2), 249–253 (2013). https://doi.org/10.7507/1001-5515.20130047

    Article  Google Scholar 

  7. Wang, J.: Analysis of motor imagery EEG based on the BP network. Tianjin Normal University (2012)

    Google Scholar 

  8. Yang, L.C., Li, J.L., Yao, Y.B., Wu, X.Q.: A P300 detection algorithm based on f-score feature selection and support vector machines. J. Biomed. Eng. 2008(1), 23–26 (2008). https://doi.org/10.1142/S0217595908001626

    Article  Google Scholar 

  9. Zhang, K.X., Lu, K.H., Zhu, J.Y., Liu, X.S., Xie, L.F.: Predicting model of algal blooms based on BP neural network. Environ. Monit. China 28(3), 53–57 (2012). https://doi.org/10.19316/j.issn.1002-6002.2012.03.012

    Article  Google Scholar 

  10. Guo, Z.M., Zhang, Z.Z., Pan, Y.X., Hua, Z.D., Feng, G.Y., He L.: Prediction of membrane protein types by using support vector machine. J. Shanghai Jiao tong Univ. 38(5) (2004). https://doi.org/10.1016/s0960-0779(03)00420-x

  11. Hagan, M.T., Beale, M., Beale, M.: Neural Network Design, pp. 8–10, PWS Publishing Co., Boston (1996). ISBN:0-534-94332-2

    Google Scholar 

  12. Cotter, N.E.: The stone-weierstrass theorem and its application to neural networks. IEEE Trans. Neural Netw. 1(4), 290–295 (1990). https://doi.org/10.1109/72.80265

    Article  MathSciNet  Google Scholar 

  13. Ran, W.L.: Soft measring technique researoh appliedin wastewater BOD based on neural computing. Beijing University of Technology (2004)

    Google Scholar 

  14. Wei, A.: Design of a classifier based on the SVM algorithm. Electron. Sci. Technol. 28(4), 23 (2015). https://doi.org/10.16180/j.cnki.issn1007-7820.2015.04.007

    Article  Google Scholar 

  15. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2011).https://doi.org/10.1145/1961189.1961199. http://www.csie.ntu.edu.tw/~cjlin/libsvm

  16. Li, K., Liu, P., Li, Y.J., Zhang, G.P., Huang, Y.H.: The parallel algorithms for LIBSVM parameter optimization based on Spark. J. Nanjing Univ. (Nat. Sci.) 52(2) (2016). https://doi.org/10.13232/j.cnki.jnju.2016.02.016

  17. Deng, J.F., Zhang, X.L.: Deep learning algorithm optimization based on combination of auto-encoders. J. Comput. Appl. 36(3), 697–702 (2016). https://doi.org/10.1172/j.issn.1001-9081.2016.03.697

    Article  Google Scholar 

  18. Guan, J.Q., Yang, B.H., Ma, S.W., Yuan, L.: Classification of motor imagery EEG based on PCA and SVM. Beijing Biomed. Eng. 29(3), 261–265 (2010). https://doi.org/10.3969/j.jssn.1002-3208.2010.03.09

    Article  Google Scholar 

  19. Modai, I., Ritsner, M., Kurs, R., Mendel, S., Ponizovsky, A.: Validation of the computerized suicide risk scale–a backpropagation neural network instrument (CSRS-BP). Eur. Psychiatry 17(2), 75–81 (2002). https://doi.org/10.1016/s0924-9338(02)00631-4

    Article  Google Scholar 

  20. Kandaswamy, K., Pugalenthi, G., Suganthan, P., Gangal, R.: SVMCRYS: an SVM approach for the prediction of protein crystallization propensity from protein sequence. Protein Peptide Lett. 17(4), 423–430 (2010). https://doi.org/10.2174/092986610790963726

    Article  Google Scholar 

  21. Dannlowski, U., Kersting, A., Lalee-Mentzel, J., Donges, U.S., Arolt, V., Suslow, T.: Subliminal affective priming in clinical depression and comorbid anxiety: a longitudinal investigation. Psychiatry Res. 143(1), 63–75 (2006). https://doi.org/10.1016/j.psychres.2005.08.022

    Article  Google Scholar 

  22. Zhang, J.D., Qin, G.H., Cui, Y.: An EEG signal system based on FastICA and SVM. J. Comput. Res. Dev. 45(z1), 255–258 (2008)

    Google Scholar 

  23. Murphy, S.T., Zajonc, R.B.: Affect, cognition, and awareness: affective priming with optimal and suboptimal stimulus exposures. J Pers. Soc. Psychol. 64(5), 723 (1993). https://doi.org/10.1037//0022-3514.64.5.723

    Article  Google Scholar 

  24. Jiang, Z.Q., Yang, L.Z., Liu, Y.: Study on adults’ subliminal affective priming effect. J. Liaoning Normal Univ. (Soc. Sci. Edn.) 28(4), 48–50 (2005). https://doi.org/10.3969/j.issn.1000-1751.2005.04.015

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250014, China

    Yongqiang Yin, Bin Hu & Xiangwei Zheng

  2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan, 250014, China

    Yongqiang Yin, Bin Hu & Xiangwei Zheng

  3. Faculty of Education, Shandong Normal University, Jinan, 250014, China

    Tiantian Li

Authors
  1. Yongqiang Yin
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  2. Bin Hu
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  3. Tiantian Li
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  4. Xiangwei Zheng
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Corresponding author

Correspondence to Bin Hu .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Kunming University of Science and Technology, Kunming, China

    Zhengtao Yu

  4. Tongji University, Shanghai, China

    Hongfei Fan

  5. University of Shanghai for Science and Technology, Shanghai, China

    Liping Gao

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Yin, Y., Hu, B., Li, T., Zheng, X. (2019). Classification of Subliminal Affective Priming Effect Based on AE and SVM. 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_60

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  • DOI: https://doi.org/10.1007/978-981-15-1377-0_60

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1376-3

  • Online ISBN: 978-981-15-1377-0

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