Abstract
Electroencephalogram (EEG) is an effective metric to monitor or measure human brain activities. Another advantage for EEG utilization is noninvasive, and is not harmful to subjects. However, this leads to two drawbacks: low signal-to-noise ratio and non-stationarity. In order to extract useful features contained in the EEG, multifractal attributes were explored in this paper. A few attributes were utilized to analyze the EEG recorded during motor imagery tasks. Then, we built a deep learning model based on denoising autoencoder to recognize different motor imagery tasks. From the results, we can find that 1) Motor imagery induced EEG is of multifractal attributes, 2) multifractal spectrum D(h) and the statistics cp based on cumulants can reflect difference between different motor imagery tasks, so they can be adopted as features for classification. 3) A deep network with initialization by denoising autoencoder is suitable to learn multifractal attributes extracted from EEG. The classification accuracies demonstrated that the proposed method is feasible.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mandelbrot, B.B.: How Long Is the Coast of Britain. Science 156(3775), 636–638 (1967)
Dutta, S., Ghosh, D., Samanta, S., Dey, S.: Multifractal Parameters as an Indication of Different Physiological and Pathological States of the Human Brain. Physica A 396, 155–163 (2014)
Oczeretko, E., Juczewska, M., Kasacka, I.: Fractal Geometric Analysis of Lung Cancer Angiogenic Patterns. Folia Histochem. Cytobiol. 39, 75–76 (2000)
Zook, J.M., Iftekharuddin, K.M.: Statistical Analysis of Fractal-based Brain Tumor Detection Algorithms. Magn. Reson. Imaging 23(5), 671–678 (2005)
Li, J., Liang, J., Zhao, Q., Li, J., Hong, K., Zhang, L.: Design of Assistive Wheelchair System Directly Steered by Human Thoughts. Int. J. Neural Syst. 23(3), 1350013 (2013)
Pfurtscheller, G., Muller, G.R., Pfurtscheller, J., Gerner, H.J., Rupp, R.: ‘Thought’-Control of Functional Electrical Stimulation to Restore Hand Grasp in a Patient with Tetraplegia. Neurosci. Lett. 351(1), 33–36 (2003)
Li, J., Zhang, L.: Active Training Paradigm for Motor Imagery BCI. Exp. Brain Res. 219(2), 245–254 (2012)
Li, J., Wang, Y., Zhang, L., Jung, T.P.: Combining ERPs and EEG Spectral Features for Decoding Intended Movement Direction. In: 34th Annual International Conference of the IEEE EMBS, San Diego, August 28-September 1, pp. 1769–1772 (2012)
Brodu, N., Lotte, F., Lécuyer, A.: Exploring Two Novel Features for EEG-based Brain–Computer Interfaces: Multifractal Cumulants and Predictive Complexity. Neurocomputing 79, 87–94 (2012)
Li, J., Liu, Y., Lu, Z., Zhang, L.: A Competitive Brain Computer Interface: Multi-person Car Racing System. In: 35th Annual International Conference of the IEEE EMBS, Osaka, Japan, July 3-7, pp. 2200–2203 (2013)
Wendt, H., Abry, P., Jaffard, S.: Bootstrap for Empirical Multifractal Analysis. IEEE Signal Process. Mag. 24(4), 38–48 (2007)
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)
Wendt, H., Abry, P.: Multifractality Tests Using Bootstrapped Wavelet Leaders. IEEE Trans. Signal Process. 55(10), 4811–4820 (2007)
Lopes, R., Betrouni, N.: Fractal and Multifractal Analysis: A Review. Med. Image Anal. 13(4), 634–649 (2009)
Li, J., Struzik, Z., Zhang, L., Cichocki, A.: Spectral Power Estimation for Unevenly Spaced Motor Imagery Data. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013, Part I. LNCS, vol. 8226, pp. 168–175. Springer, Heidelberg (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, J., Cichocki, A. (2014). Deep Learning of Multifractal Attributes from Motor Imagery Induced EEG. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_63
Download citation
DOI: https://doi.org/10.1007/978-3-319-12637-1_63
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12636-4
Online ISBN: 978-3-319-12637-1
eBook Packages: Computer ScienceComputer Science (R0)