Abstract
Finding effective representations from electroencephalogram (EEG) data is challenging. Complex network (CN) analysis has been proved to be one of the efficient way in the EEG time series analysis, such as modeling the cognitive events of human beings. But most of the network analysis is just using the time domain statistical features and often has a fixed threshold for the network’s connectivity. Herein, based on our previous work with an adaptive threshold, we propose a novel approach using a set of thresholds which fit to the data distribution to construct connections between different EEG channels to generate a multi-channel network. Inspired by the multi-frame method of video processing, we also divide the EEG data of one trial into several frames without overlap. The final classification is based on the multi-threshold and multi-frame network structural features. The results on the cognitive load classification dataset demonstrate that the proposed approach is more efficient than the deep learning method, and reduce the mean classification error to 8.1%.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Backes, A.R., Casanova, D., Bruno, O.M.: Texture analysis and classification: a complex network-based approach. Inf. Sci. 219(1), 168–180 (2013)
Bashivan, P., Rish, I., Yeasin, M., Codella, N.: Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv preprint arxiv:1511.06448 (2015)
Diykh, M., Li, Y.: Complex networks approach for EEG signal sleep stages classification. Expert Syst. Appl. 63, 241–248 (2016)
Güler, N.F., Übeyli, E.D., Güler, I.: Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst. Appl. 29(3), 506–514 (2005)
He, L., Liu, B., Hu, D., Wen, Y., Wan, M., Long, J.: Motor imagery EEG signals analysis based on Bayesian network with Gaussian distribution. Neurocomputing 188, 217–224 (2016)
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1725–1732 (2014)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT press, Cambridge (2009)
Kovalev, V.A., Kruggel, F., Gertz, H.J., von Cramon, D.Y.: Three-dimensional texture analysis of MRI brain datasets. IEEE Trans. Med. Imaging 20(5), 424–433 (2001)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances In Neural Information Processing Systems, pp. 1097–1105 (2012)
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4(2), 1–24 (2007). R1
Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of EEG synchronization for seizure prediction. Clin. Neurophysiol. 120(11), 1927–1940 (2009)
Shang, J., Zhang, W., Xiong, J., Liu, Q.: Cognitive load recognition using multi-channel complex network method. In: Cong, F., Leung, A., Wei, Q. (eds.) ISNN 2017. LNCS, vol. 10261, pp. 466–474. Springer, Cham (2017). doi:10.1007/978-3-319-59072-1_55
Spampinato, C., Palazzo, S., Kavasidis, I., Giordano, D., Shah, M., Souly, N.: Deep learning human mind for automated visual classification. arXiv preprint arxiv:1609.00344 (2016)
Sweller, J., Van Merrienboer, J.J., Paas, F.G.: Cognitive architecture and instructional design. Educ. Psychol. Rev. 10(3), 251–296 (1998)
Tao, D., Li, X., Wu, X., Maybank, S.J.: General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(10), 1700–1715 (2007)
Wang, J., Yang, C., Wang, R., Yu, H., Cao, Y., Liu, J.: Functional brain networks in Alzheimers disease: EEG analysis based on limited penetrable visibility graph and phase space method. Physica A 460, 174–187 (2016)
Zhang, J., Small, M.: Complex network from pseudoperiodic time series: topology versus dynamics. Phys. Rev. Lett. 96(23), 238701 (2006)
Zheng, W.L., Lu, B.L.: Investigating critical frequency bands and channels for eeg-based emotion recognition with deep neural networks. IEEE Trans. Auton. Ment. Dev. 7(3), 162–175 (2015)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 61473333.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Shang, J., Liu, Q. (2017). Cognitive Load Recognition Using Multi-threshold United Complex Network. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-70136-3_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70135-6
Online ISBN: 978-3-319-70136-3
eBook Packages: Computer ScienceComputer Science (R0)