Abstract
Tinnitus is an auditory phantom percept of chronic high-pitched sound, ringing, or noise. Since the underlying physiological mechanisms of tinnitus are still under study, there is no universally effective treatment to cure tinnitus so far. There is even no method for objectively classifying tinnitus patients from normal people. In this paper, we utilize a Multi-view Intact Space Learning (MISL) method for the analysis and classification of electroencephalogram (EEG) signals using power value of frequency bands. At first, the power values of seven frequency bands are calculated by using Fast Fourier Transform (FFT) so as to obtain seven single views of features. Next, Multi-view Intact Space Learning is applied to integrate the seven single views together to get better classification results. Compared with the single view classification, the Multi-view Intact Space Learning method has achieved significant accuracy improvements by 6.32–23.25%. That is, the best accuracy, precision, recall and F1 of classification performance reach 0.828, 0.811, 0.857 and 0.833 respectively. The proposed method can be applied for auxiliary therapy of tinnitus as well as be extended to assist with the treatment of other diseases.
Shao-Ju Wang and Yue-Xin Cai make equal contributions.
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
Similar content being viewed by others
References
Basoeki, A., Rahardjo, E., Hood, J.: PCA-based linear dynamical systems for multichannel EEG classification. In: International Conference on Neural Information Processing (ICONIP), vol. 2, pp. 745–749 (2002)
Eggermont, J.J., Roberts, L.E.: The neuroscience of tinnitus. Trends Neurosci. 27(11), 676–682 (2004)
Ghayab, H.R.A., Li, Y., Abdulla, S., Diykh, M., Wan, X.: Classification of epileptic EEG signals based on simple random sampling and sequential feature selection. Brain Inform. 3(2), 85–91 (2016)
Houdayer, E., Teggi, R., Velikova, S., Gonzalez-Rosa, J., Bussi, M., Comi, G., Leocani, L.: Involvement of cortico-subcortical circuits in normoacousic chronic tinnitus: a source localization EEG study. Clin. Neurophysiol. 126(12), 2356–2365 (2015)
Iriarte, J., Urrestarazu, E., Valencia, M., Alegre, M., Malanda, A., Viteri, C., Artieda, J.: Independent component analysis as a tool to eliminate artifacts in EEG: a quantitative study. J. Clin. Neurophysiol. 20(4), 249 (2003)
Li, P.-Z., Li, J.-H., Wang, C.-D.: A SVM-based EEG signal analysis: an auxiliary therapy for tinnitus. In: Liu, C.-L., Hussain, A., Luo, B., Tan, K.C., Zeng, Y., Zhang, Z. (eds.) BICS 2016. LNCS, vol. 10023, pp. 207–219. Springer, Cham (2016). doi:10.1007/978-3-319-49685-6_19
Lin, K.-Y., Wang, C.-D., Meng, Y.-Q., Zhao, Z.-L.: Multi-view unit intact space learning. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds.) KSEM 2017. LNCS, vol. 10412, pp. 211–223. Springer, Cham (2017). doi:10.1007/978-3-319-63558-3_18
Meyer, M., Luethi, M.S., Neff, P., Langer, N., Büchi, S.: Disentangling tinnitus distress and tinnitus presence by means of EEG power analysis. Neural Plast. 2014 (2014)
Perera, H., Shiratuddin, M.F., Wong, K.W.: A review of electroencephalogram-based analysis and classification frameworks for dyslexia. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9950, pp. 626–635. Springer, Cham (2016). doi:10.1007/978-3-319-46681-1_74
Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation (2011)
Roberts, L.E., Eggermont, J.J., Caspary, D.M., Shore, S.E., Melcher, J.R., Kaltenbach, J.A.: Ringing ears: the neuroscience of tinnitus. J. Neurosci. 30(45), 14972–14979 (2010)
Singh, P., Joshi, S., Patney, R., Saha, K.: Fourier-based feature extraction for classification of EEG signals using EEG rhythms. Circ. Syst. Sig. Process. 35(10), 3700–3715 (2016)
Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific, Singapore (2002)
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Vanneste, S., De, R.D.: Deafferentation-based pathophysiological differences in phantom sound: tinnitus with and without hearing loss. Neuroimage 129, 80–94 (2015)
Wu, W., Chen, Z., Gao, X., Li, Y., Brown, E.N., Gao, S.: Probabilistic common spatial patterns for multichannel EEG analysis. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 639 (2015)
Xu, C., Tao, D., Xu, C.: Multi-view intact space learning. IEEE Trans. Pattern Anal. Mach. Intell. 37(12), 2531–2544 (2015)
Acknowledgment
This work was supported by NSFC (No. 61502543) and Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (No. 2016TQ03X542).
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
Wang, SJ., Cai, YX., Sun, ZR., Wang, CD., Zheng, YQ. (2017). Tinnitus EEG Classification Based on Multi-frequency Bands. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_84
Download citation
DOI: https://doi.org/10.1007/978-3-319-70093-9_84
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70092-2
Online ISBN: 978-3-319-70093-9
eBook Packages: Computer ScienceComputer Science (R0)