Multi-View Intact Space Learning for Tinnitus Classification in Resting State EEG

  • Zhi-Ran Sun
  • Yue-Xin Cai
  • Shao-Ju Wang
  • Chang-Dong Wang
  • Yi-Qing Zheng
  • Yan-Hong Chen
  • Yu-Chen Chen


Tinnitus is a common but obscure auditory disease to be studied, and there are still in the lack of effective methods developed to treat tinnitus universally. Although electroencephalogram (EEG) is widely applied to the diagnosis of tinnitus, there are few machine learning methods developed to classify tinnitus patients from healthy people based on the EEG data. Moreover, there is still room for improving the classification performance due to the insufficient existing studies. Therefore, in order to improve the performance of classification based on the EEG data, we introduce a multi-view intact space learning method to characterize the EEG signals by feature extraction in a latent intact space. Considering the fact that there are only a small number of subjects available for study, we conduct the classification for valid segments of EEG data of each subject. In this way, the dataset can be enlarged and the classification performance can be improved. By combining different views of EEG data, a considerable result is achieved on classification by using Support Vector Machine classifier, with accuracy, recall, precision, F1 to be 99.23, 99.72, 98.97, 99.34% respectively. This proposed method is an effective and objective method to classify the tinnitus patients from healthy people, further researches are needed to explore the machine learning method in classification and prediction of the effectiveness of tinnitus interventions based on the EEG response of tinnitus individuals.


Tinnitus Classification Multi-view learning 



We would like to acknowledge Dr. Fei Zhao for his proof reading.


This work was funded by National Natural Science Foundation of China (Grant No. 81600808), Natural Science Foundation of Guangdong Province (Grant No. 2016A030313318 and 2015A030310134), Guangdong Natural Science Funds for Distinguished Young Scholar (2016A030306014) and Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (2016TQ03X542).

Compliance with Ethical Standards

Conflict of interest

The authors declared that they have no conflict of interest.


  1. 1.
    Akin M (2002) Comparison of wavelet transform and FFT methods in the analysis of EEG signals. J Med Syst 26(3):241–247CrossRefGoogle Scholar
  2. 2.
    Al Ghayab HR, Li Y, Abdulla S, Diykh M, Wan X (2016) Classification of epileptic EEG signals based on simple random sampling and sequential feature selection. Brain Inform 3(2):85–91CrossRefGoogle Scholar
  3. 3.
    Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167CrossRefGoogle Scholar
  4. 4.
    Chen YC, Zhang J, Li XW, Xia W, Feng X, Qian C, Yang XY, Lu CQ, Wang J, Salvi R (2015) Altered intra-and interregional synchronization in resting-state cerebral networks associated with chronic tinnitus. Neural Plast 2015:475382CrossRefGoogle Scholar
  5. 5.
    Emami Y, Bayrak C (2017) EEG analysis of evoked potentials of the brain to develop a mathematical model for classifying tinnitus datasets. In: 2017 IEEE international symposium on medical measurements and applications (MeMeA), IEEE, pp 379–384Google Scholar
  6. 6.
    Han J, Chen H, Liu N, Yan C, Li X (2017) CNNs-based rgb-d saliency detection via cross-view transfer and multiview fusion. IEEE Trans Cybern 99:1–13Google Scholar
  7. 7.
    Han J, Quan R, Zhang D, Nie F (2018) Robust object co-segmentation using background prior. IEEE Trans Image Process 27(4):1639–1651MathSciNetCrossRefGoogle Scholar
  8. 8.
    Han J, Zhang D, Cheng G, Liu N, Xu D (2018) Advanced deep-learning techniques for salient and category-specific object detection: a survey. IEEE Signal Process Mag 35(1):84–100CrossRefGoogle Scholar
  9. 9.
    Hong R, Hu Z, Wang R, Wang M, Tao D (2016) Multi-view object retrieval via multi-scale topic models. IEEE Trans Image Process 25(12):5814–5827MathSciNetCrossRefGoogle Scholar
  10. 10.
    Hong R, Wang M, Gao Y, Tao D, Li X, Wu X (2014) Image annotation by multiple-instance learning with discriminative feature mapping and selection. IEEE Trans Cybern 44(5):669–680CrossRefGoogle Scholar
  11. 11.
    Houdayer E, Teggi R, Velikova S, Gonzalez-Rosa J, Bussi M, Comi G, Leocani L (2015) Involvement of cortico-subcortical circuits in normoacousic chronic tinnitus: a source localization EEG study. Clin Neurophysiol 126(12):2356–2365CrossRefGoogle Scholar
  12. 12.
    Huang L, Chao HY, Wang CD (2017) Multi-view intact space clustering. In: Proceedings of the 4th Asian conference on pattern recognition, pp 500–505Google Scholar
  13. 13.
    Kim SI, Kim MG, Kim SS, Byun JY, Park MS, Yeo SG (2016) Evaluation of tinnitus patients by audiometric configuration. Am J Otolaryngol 37(1):1–5CrossRefGoogle Scholar
  14. 14.
    Li JH, Wang CD, Li PZ, Lai JH (2018) Discriminative metric learning for multi-view graph partitioning. Pattern Recogn 75:199–213CrossRefGoogle Scholar
  15. 15.
    Li PZ, Cai YX, Wang CD, Liang MJ, Zheng YQ (2018) Higher-order brain network analysis for auditory disease. Neural Process Lett.
  16. 16.
    Li PZ, Li JH, Wang CD (2016) A SVM-based EEG signal analysis: an auxiliary therapy for tinnitus. In: Proceedings of advances in brain inspired cognitive systems: 8th international conference, BICS 2016, Beijing, China, November 28–30, 2016, vol 8. Springer, pp 207–219Google Scholar
  17. 17.
    Li X, Chen X, Yan Y, Wei W, Wang ZJ (2014) Classification of EEG signals using a multiple kernel learning support vector machine. Sensors 14(7):12784–12802CrossRefGoogle Scholar
  18. 18.
    Lin KY, Huang L, Wang CD, Chao HY (2018) Multi-view proximity learning for clustering. In: Proceedings of the 23rd international conference on database systems for advanced applicationsGoogle Scholar
  19. 19.
    Lin KY, Wang CD, Meng YQ, Zhao ZL (2017) Multi-view unit intact space learning. In: 10th international conference on knowledge science, engineering and management, pp 211–223Google Scholar
  20. 20.
    Manzano M, Guillén A, Rojas I, Herrera LJ (2017) Deep learning using EEG data in time and frequency domains for sleep stage classification. In: International work-conference on artificial neural networks. Springer, pp 132–141Google Scholar
  21. 21.
    Meyer M, Luethi MS, Neff P, Langer N, Büchi S (2014) Disentangling tinnitus distress and tinnitus presence by means of EEG power analysis. Neural Plast 2014:1–13Google Scholar
  22. 22.
    Moazami-Goudarzi M, Michels L, Weisz N, Jeanmonod D (2010) Temporo-insular enhancement of EEG low and high frequencies in patients with chronic tinnitus. QEEG study of chronic tinnitus patients. BMC Neurosci 11(1):40CrossRefGoogle Scholar
  23. 23.
    Palomares I, Browne F, Davis P (2017) Multi-view fuzzy information fusion in collaborative filtering recommender systems: application to the urban resilience domain. Data Knowl Eng 113:64–80CrossRefGoogle Scholar
  24. 24.
    Polat K, Güneş S (2008) Artificial immune recognition system with fuzzy resource allocation mechanism classifier, principal component analysis and FFT method based new hybrid automated identification system for classification of eeg signals. Expert Syst Appl 34(3):2039–2048CrossRefGoogle Scholar
  25. 25.
    Singh P, Joshi S, Patney R, Saha K (2016) Fourier-based feature extraction for classification of EEG signals using EEG rhythms. Circuits Syst Signal Process 35(10):3700–3715MathSciNetCrossRefGoogle Scholar
  26. 26.
    Subasi A, Gursoy MI (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37(12):8659–8666CrossRefGoogle Scholar
  27. 27.
    Vanneste S, De Ridder D (2016) Deafferentation-based pathophysiological differences in phantom sound: tinnitus with and without hearing loss. Neuroimage 129:80–94CrossRefGoogle Scholar
  28. 28.
    Wang CD, Lai JH, Yu PS (2016) Multi-view clustering based on belief propagation. IEEE Trans Knowl Data Eng 28(4):1007–1021CrossRefGoogle Scholar
  29. 29.
    Wang SJ, Cai YX, Sun ZR, Wang CD, Zheng YQ (2017) Tinnitus EEG classification based on multi-frequency bands. In: International conference on neural information processing. Springer, pp 788–797Google Scholar
  30. 30.
    Wang XW, Nie D, Lu BL (2014) Emotional state classification from EEG data using machine learning approach. Neurocomputing 129:94–106CrossRefGoogle Scholar
  31. 31.
    Xu C, Tao D, Xu C (2015) Multi-view intact space learning. IEEE Trans Pattern Anal Mach Intell 37(12):2531–2544CrossRefGoogle Scholar
  32. 32.
    Xu H, Plataniotis KN (2016) Affective states classification using EEG and semi-supervised deep learning approaches. In: 2016 IEEE 18th international workshop on multimedia signal processing (MMSP). IEEE, pp 1–6Google Scholar
  33. 33.
    Xu L, Wang CD, Liang MJ, Cai YX, Zheng YQ (2018) Brain network regional synchrony analysis in deafness. Biomed Res Int.
  34. 34.
    Xu YM, Wang CD, Lai JH (2016) Weighted multi-view clustering with feature selection. Pattern Recogn 53:25–35CrossRefGoogle Scholar
  35. 35.
    Yao X, Han J, Zhang D, Nie F (2017) Revisiting co-saliency detection: a novel approach based on two-stage multi-view spectral rotation co-clustering. IEEE Trans Image Process 26(7):3196–3209MathSciNetCrossRefGoogle Scholar
  36. 36.
    Yildirim A, Halici U (2013) Analysis of dimension reduction by PCA and adaboost on spelling paradigm EEG data. In: 2013 6th international conference on biomedical engineering and informatics (BMEI). IEEE, pp 192–196Google Scholar
  37. 37.
    Yu X, Chum P, Sim KB (2014) Analysis the effect of PCA for feature reduction in non-stationary EEG based motor imagery of BCI system. Optik Int J Light Electron Opt 125(3):1498–1502CrossRefGoogle Scholar
  38. 38.
    Zhang GY, Wang CD, Huang D, Zheng WS (2017) Multi-view collaborative locally adaptive clustering with minkowski metric. Expert Syst Appl 86:307–320CrossRefGoogle Scholar
  39. 39.
    Zhang GY, Wang CD, Huang D, Zheng WS, Zhou YR (2018) TW-Co-k-means: two-level weighted collaborative k-means for multi-view clustering. Knowl Based Syst.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  2. 2.Otolaryngology, Sun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhouChina
  3. 3.Institute of Hearing and Speech-Language ScienceSun Yat-sen UniversityGuangzhouChina
  4. 4.Zhongshan School of MedicineSun Yat-sen UniversityGuangzhouChina
  5. 5.Department of Radiology, Nanjing First HospitalNanjing Medical UniversityNanjingChina

Personalised recommendations