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.
This project is supported by the FRGS research grant schemes FRG0349 & FRG0435 from the Ministry of Higher Education, Malaysia.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chew, L.H., Teo, J., Mountstephens, J.: Aesthetic preference recognition of 3D shapes using EEG. Cogn. Neurodyn. 10(2), 165–173 (2016)
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 2017
Wang, X.W., Nie, D., Lu, B.L.: Emotional state classification from EEG data using machine learning approach. Neurocomputing 129, 94–106 (2014)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Gielis, J.: A generic geometric transformation that unifies a wide range of natural and abstract shapes. Am. J. Bot. 90(3), 333–338 (2003)
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)
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)
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)
Zeiler, M.D.: Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701
Acknowledgements
This project is supported by the FRGS research grant scheme ref: FRG0435 from the Ministry of Higher Education, Malaysia.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Teo, J., Chew, L.H., Mountstephens, J. (2019). Improving Subject-Independent EEG Preference Classification Using Deep Learning Architectures with Dropouts. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 886. Springer, Cham. https://doi.org/10.1007/978-3-030-03402-3_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-03402-3_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03401-6
Online ISBN: 978-3-030-03402-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)