Advertisement

Improving Subject-Independent EEG Preference Classification Using Deep Learning Architectures with Dropouts

  • Jason Teo
  • Lin Hou Chew
  • James Mountstephens
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 886)

Abstract

Human preferences play a key role in numerous decision-making processes. The ability to correctly identify likes and dislikes would facilitate novel applications in neuromarketing, affective entertainment, virtual rehabilitation and forensic neuroscience that leverage on sub-conscious human preferences. In this neuroinformatics investigation, we seek to recognize human preferences passively through the use of electroencephalography (EEG) when a subject is presented with some 3D visual stimuli. Our approach employs the use of machine learning in the form of deep neural networks to classify brain signals acquired using a brain-computer interface (BCI). Our previous work has shown that EEG preference classification is possible although accuracy rates remain relatively low at 61%–67% using conventional deep learning neural architectures, where the challenge mainly lies in the accurate classification of unseen data from a cohort-wide sample that introduces inter-subject variability on top of the existing intra-subject variability. Such an approach is significantly more challenging and is known as subject-independent EEG classification as opposed to the more commonly adopted but more time-consuming and less general approach of subject-dependent EEG classification. In this new study, we employ deep networks that allow dropouts to occur in the architecture of the neural network. The results obtained through this simple feature modification achieved a classification accuracy of up to 79%. Therefore, this study has shown that the use of a deep learning classifier was able to achieve an increase in emotion classification accuracy of between 13%–18% through the simple adoption of the use of dropouts compared to a conventional deep learner for EEG preference classification.

Keywords

Neuroinformatics Emotion classification Preference classification Electroencephalography (EEG) Deep learning Dropouts 

Notes

Acknowledgements

This project is supported by the FRGS research grant scheme ref: FRG0435 from the Ministry of Higher Education, Malaysia.

References

  1. 1.
    Chew, L.H., Teo, J., Mountstephens, J.: Aesthetic preference recognition of 3D shapes using EEG. Cogn. Neurodyn. 10(2), 165–173 (2016)CrossRefGoogle Scholar
  2. 2.
    Teo, J., Chew, L.H., Mountstephens, J.: Deep learning for EEG-based preference classification. In: International Conference on Applied Science and Technology (ICAST 2017). IEEE, April 2017Google Scholar
  3. 3.
    Wang, X.W., Nie, D., Lu, B.L.: Emotional state classification from EEG data using machine learning approach. Neurocomputing 129, 94–106 (2014)CrossRefGoogle Scholar
  4. 4.
    Dhall, A., Goecke, R., Joshi, J., Sikka, K., Gedeon, T.: Emotion recognition in the wild challenge 2014: baseline, data and protocol. In: Proceedings of the 16th International Conference on Multimodal Interaction, pp. 461–466. ACM (2014)Google Scholar
  5. 5.
    Verma, G.K., Tiwary, U.S.: Multimodal fusion framework: a multiresolution approach for emotion classification and recognition from physiological signals. NeuroImage 102, 162–172 (2014)CrossRefGoogle Scholar
  6. 6.
    Jang, E.H., Park, B.J., Kim, S.H., Chung, M.A., Park, M.S., Sohn, J.H.: Emotion classification based on bio-signals emotion recognition using machine learning algorithms. In: International Conference on Information Science, Electronics and Electrical Engineering (ISEEE), vol. 3, pp. 1373–1376. IEEE (2014)Google Scholar
  7. 7.
    Hadjidimitriou, S.K., Zacharakis, A.I., Doulgeris, P.C., Panoulas, K.J., Hadjileontiadis, L.J., Panas, S.M.: Revealing action representation processes in audio perception using fractal EEG analysis. IEEE Trans. Biomed. Eng. 58(4), 1120–1129 (2011)CrossRefGoogle Scholar
  8. 8.
    Adamos, D.A., Dimitriadis, S.I., Laskaris, N.A.: Towards the bio-personalization of music recommendation systems: a single-sensor EEG biomarker of subjective music preference. Inf. Sci. 343, 94–108 (2016)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Yadava, M., Kumar, P., Saini, R., Roy, P.P., Dogra, D.P.: Analysis of EEG signals and its application to neuromarketing. Multimed. Tools Appl. 1–25 (2017)Google Scholar
  10. 10.
    Chen, L.C., Sandmann, P., Thorne, J.D., Herrmann, C.S., Debener, S.: Association of concurrent fNIRS and EEG signatures in response to auditory and visual stimuli. Brain Topogr. 28(5), 710–725 (2015)CrossRefGoogle Scholar
  11. 11.
    Goncalves, S., De Munck, J., Pouwels, P., Schoonhoven, R., Kuijer, J., Maurits, N., Hoogduin, J., Van Someren, E., Heethaar, R., Da Silva, F.L.: Correlating the alpha rhythm to bold using simultaneous EEG/FMRI: inter-subject variability. Neuroimage 30(1), 203–213 (2006)CrossRefGoogle Scholar
  12. 12.
    Pfurtscheller, G., Brunner, C., Schlogl, A., Da Silva, F.L.: Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 31(1), 153–159 (2006)CrossRefGoogle Scholar
  13. 13.
    Yazdani, A., Lee, J.S., Vesin, J.-M., Ebrahimi, T.: A ECT recognition based on physiological changes during the watching of music video. ACM Trans. Interact. Intell. Syst. 2(EPFL-ARTICLE-177741), 1–26 (2012)CrossRefGoogle Scholar
  14. 14.
    Pan, Y., Guan, C., Yu, J., Ang, K.K., Chan, T.E.: Common frequency pattern for music preference identification using frontal EEG. In: 6th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 505–508. IEEE (2013)Google Scholar
  15. 15.
    Tseng, K.C., Lin, B.-S., Han, C.-M., Wang, P.-S.: Emotion recognition of EEG underlying favourite music by support vector machine. In: International Conference on Orange Technologies (ICOT), pp. 155–158. IEEE (2013)Google Scholar
  16. 16.
    Kim, Y., Kang, K., Lee, H., Bae, C.: Preference measurement using user response electroencephalogram. In: Computer Science and Its Applications, pp. 1315–1324. Springer (2015)Google Scholar
  17. 17.
    Hadjidimitriou, S.K., Hadjileontiadis, L.J.: Toward an EEG-based recognition of music liking using time-frequency analysis. IEEE Trans. Biomed. Eng. 59(12), 3498–3510 (2012)CrossRefGoogle Scholar
  18. 18.
    Hadjidimitriou, S.K., Hadjileontiadis, L.J.: EEG-based classification of music appraisal responses using time-frequency analysis and familiarity ratings. IEEE Trans. Affect. Comput. 4(2), 161–172 (2013)CrossRefGoogle Scholar
  19. 19.
    Moon, J., Kim, Y., Lee, H., Bae, C., Yoon, W.C.: Extraction of user preference for video stimuli using EEG-based user responses. ETRI J. 35(6), 1105–1114 (2013)CrossRefGoogle Scholar
  20. 20.
    Li, K., Li, X., Zhang, Y., Zhang, A.: Affective state recognition from EEG with deep belief networks. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 305–310. IEEE (2013)Google Scholar
  21. 21.
    Zheng, W.-L., Zhu, J.-Y., Peng, Y., Lu, B.-L.: EEG-based emotion classification using deep belief networks. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2014)Google Scholar
  22. 22.
    Jirayucharoensak, S., Pan-Ngum, S., Israsena, P.: EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation. Sci. World J. (2014)Google Scholar
  23. 23.
    Gielis, J.: A generic geometric transformation that unifies a wide range of natural and abstract shapes. Am. J. Bot. 90(3), 333–338 (2003)CrossRefGoogle Scholar
  24. 24.
    Nguyen, D., Widrow, B.: Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In: IJCNN International Joint Conference on Neural Networks, pp. 21–26. IEEE (1990)Google Scholar
  25. 25.
    De Boer, P.T., Kroese, D.P., Mannor, S., Rubinstein, R.Y.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1), 19–67 (2005)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-2010), pp. 807–814 (2010)Google Scholar
  27. 27.
    Zeiler, M.D.: Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Computing and InformaticsUniversiti Malaysia SabahKota KinabaluMalaysia

Personalised recommendations