Abstract
Electroencephalogram (EEG)-based emotion recognition has been widely researched in the field of affective computing. Nevertheless, EEG signals which reflect brain activity are always unstable, it is inappropriate for traditional analysis methods to treat each sliding time window of signals as independent sample during classification. In this study, we employ a multi-instance learning (MIL) framework for EEG-based emotion recognition and regard sliding time windows from the same EEG signal as a whole by learning two MIL models based on Citation-kNN and mi-SVM algorithms. Experiment results show that our methods can achieve higher classification accuracy of 74.21% and 77.50% on two affective dimensions (valence and arousal) respectively when comparing with traditional single-instance classification algorithms. We believe that MIL framework can improve the generalization performance of EEG-based emotion recognition further, and provide new inspiration for affective computing.
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References
Picard, R.W.: Affective Computing, vol. 1, 1st edn, pp. 71–73. IGI Global, Hershey (1997)
Calvo, R.A., D’Mello, S.: Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Trans. Affect. Comput. 1(1), 18–37 (2010)
Tkalčič, M., Burnik, U., Košir, A.: Using affective parameters in a content-based recommender system for images. User Model. User-Adapt. Interact. 20(4), 279–311 (2010)
Anderson, K., Mcowan, P.W.: A real-time automated system for the recognition of human facial expressions. IEEE Trans. Syst. Man Cybern. Part B Cybern. Publ. IEEE Syst. Man Cybern. Soc. 36(1), 96–105 (2006)
van der Wal, C.N., Kowalczyk, W.: Detecting changing emotions in human speech by machine and humans. Appl. Intell. 39(4), 675–691 (2013)
Wagner, J., Kim, N.J., Andre, E.: From physiological signals to emotions: implementing and comparing selected methods for feature extraction and classification. In: IEEE International Conference on Multimedia and Expo, pp. 940–943. IEEE (2005)
Mao, C., et al.: EEG-based biometric identification using local probability centers. In: International Joint Conference on Neural Networks, pp. 1–8. IEEE (2015)
Chen, J., et al.: Feature-level fusion of multimodal physiological signals for emotion recognition. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 395–399. IEEE (2015)
Paus, T., Sipila, P.K., Strafella, A.P.: Synchronization of neuronal activity in the human primary motor cortex by transcranial magnetic stimulation: an EEG study. J. Neurophysiol. 86(4), 1983–1990 (2001)
Chanel, G., et al.: Short-term emotion assessment in a recall paradigm. Int. J. Hum Comput Stud. 67(8), 607–627 (2009)
Kaplan, A.Y., et al.: Nonstationary nature of the brain activity as revealed by EEG/MEG: methodological, practical and conceptual challenges. Signal Process. 85(11), 2190–2212 (2005)
Sanei, S., Chambers, J.A.: EEG signal processing. In: The Fernow Watershed Acidification Study, pp. 207–236. Springer, Netherlands (2013)
Sadatnejad, K., et al.: EEG Representation Using Multi-instance Framework on The Manifold of Symmetric Positive Definite Matrices for EEG-based Computer Aided Diagnosis (2017)
Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1–2), 31–71 (1997)
Kandemir, M., Hamprecht, F.A.: Computer-aided diagnosis from weak supervision: a benchmarking study. Comput. Med. Imaging Graph. 42, 44–50 (2015)
Huo, J., Gao, Y., Yang, W., Yin, H.: Abnormal event detection via multi-instance dictionary learning. In: Yin, H., Costa, J.A.F., Barreto, G. (eds.) IDEAL 2012. LNCS, vol. 7435, pp. 76–83. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32639-4_10
Fang, Y., Chang, L.: Multi-instance feature learning based on sparse representation for facial expression recognition. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds.) MMM 2015. LNCS, vol. 8935, pp. 224–233. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14445-0_20
Lee, C.-C., et al.: Affective state recognition in married couples’ interactions using PCA-based vocal entrainment measures with multiple instance learning. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011. LNCS, vol. 6975, pp. 31–41. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24571-8_4
Wu, B., et al.: Music emotion recognition by multi-label multi-layer multi-instance multi-view learning. In: ACM International Conference on Multimedia, pp. 117–126. ACM (2014)
Jafari, A., et al.: An EEG artifact identification embedded system using ICA and multi-instance learning. In: IEEE International Symposium on Circuits and Systems, pp. 1–4. IEEE (2017)
Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. In: Advances in Neural Information Processing Systems, vol. 200, no. 2, pp. 570–576 (1998)
Weidmann, N., Frank, E., Pfahringer, B.: A two-level learning method for generalized multi-instance problems. In: Lavrač, N., Gamberger, D., Blockeel, H., Todorovski, L. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 468–479. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39857-8_42
Wang, J., Zucker, J.D.: Solving the multiple-instance problem: a lazy learning approach. In: Seventeenth International Conference on Machine Learning, pp. 1119–1126. Morgan Kaufmann Publishers Inc. (2000)
Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Advances in Neural Information Processing Systems, vol. 15, no. 2, pp. 561–568 (2002)
Koelstra, S., et al.: DEAP: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)
Bos, D.O.: EEG-based Emotion Recognition (2008)
Lang, P.J.: The emotion probe. Studies of motivation and attention. Am. Psychol. 50(5), 372 (1995)
Zhao, Q., et al.: Automatic identification and removal of ocular artifacts in EEG–improved adaptive predictor filtering for portable applications. IEEE Trans. Nanobiosci. 13(2), 109–117 (2014)
Seitsonen, E.R., et al.: EEG spectral entropy, heart rate, photoplethysmography and motor responses to skin incision during sevoflurane anaesthesia. Acta Anaesthesiol. Scand. 49(3), 284–292 (2005)
Inuso, G., et al.: Brain activity investigation by EEG processing: wavelet analysis, kurtosis and Renyi’s entropy for artifact detection. In: International Conference on Information Acquisition, pp. 195–200. IEEE (2007)
Acknowledgement
This work was supported by the National Basic Research Program of China (973 Program) (No.2014CB744600), the state key development program of China (No.2017YFE0111900), the National Natural Science Foundation of China (grant No.61402211, No.61210010) and the Fundamental Research Funds for the Central Universities (lzujbky-2017-196, lzujbky-2017-kb08). The authors acknowledge European Community’s Seventh Framework Program (FP7/2007-2011) for their DEAP database.
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Zhang, X. et al. (2018). Emotion Recognition Based on Electroencephalogram Using a Multiple Instance Learning Framework. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_66
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