Skip to main content
SpringerLink
Log in
Book cover

Australasian Database Conference

ADC 2017: Databases Theory and Applications pp 151–164Cite as

A New Data Mining Scheme for Analysis of Big Brain Signal Data

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10538))

Abstract

Analysis and processing of brain signal data (e.g. Electroencephalogram (EEG) data) is a significant challenge in the medical data mining community due to its massive size and dynamic nature. The most crucial part of EEG data analysis is to discover hidden knowledge from a large volume of data through pattern mining for efficient analysis. This study focuses on discovering representative patterns from each channel data to recover useful information reducing the size of data. In this paper, a novel algorithm based on principal component analysis (PCA) technique is developed to accurately and efficiently extract a pattern from the vast amount of EEG signal data. This study considers PCA to explore the sequential pattern of each EEG channel data as PCA is a dominant tool for finding patterns in it. In order to represent the distribution of the pattern, the most significant satanical features (e.g. mean, standard deviation) are computed from the extracted pattern. Then aggregating all of the features extracted from each of the patterns in a subject, a feature vector set is created that is fed into random forest (RF) and random tree (RT) classification model, individually for classifying different categories of the signals. The proposed methodology is tested on two benchmark EEG datasets of BCI Competition III: dataset V and dataset IVa. In order to further evaluate performance, the proposed scheme is compared with some recently reported algorithms, where the same datasets were used. The experimental results demonstrate that the proposed methodology with RF achieves higher performance compared to the RT and also the recently reported methods. The present study suggests the merits and feasibility of applying proposed method in the medical data mining for efficient analysis of any biomedical signal data.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. BCI Competition III. http://www.bbci.de/competition/iii

  2. Bentlemsan, M., Zemouri, E.T., Bouchaffra, D., Yahya-Zoubir, B., Ferroudji, K.: Random forest and filter bank common spatial patterns for eeg-based motor imagery classification. In: 5th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pp. 235–238. IEEE (2014)

    Google Scholar 

  3. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  4. Chen, W., Wang, Y., Cao, G., Chen, G., Gu, Q.: A random forest model based classification scheme for neonatal amplitude-integrated eeg. Biomed. Eng. online 13(2), S4 (2014)

    Article  Google Scholar 

  5. Chiappa, S., Millán, J.: Data set v (mental imagery, multi-class). IDIAP Research Institute, Switzerland (2005). http://ida.first.fraunhofer.de/projects/bci/competition_iii/desc_V.htmli

  6. De Veaux, R.D., Velleman, P.F., Bock, D.E.: Intro Stats, 3rd edn. Pearson/Addison Wesley, Boston (2008)

    Google Scholar 

  7. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, New York (2012)

    MATH  Google Scholar 

  8. Frank, E., et al.: Weka-a machine learning workbench for data mining. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook. Springer, Boston (2009). doi:10.1007/978-0-387-09823-4_66

    Google Scholar 

  9. Hassan, A.R., Siuly, S., Zhang, Y.: Epileptic seizure detection in eeg signals using tunable-q factor wavelet transform and bootstrap aggregating. Comput. Methods Progr. Biomed. 137, 247–259 (2016)

    Article  Google Scholar 

  10. Ines, H., Slim, Y., Noureddine, E.: EEG classification using support vector machine. In: 10th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 1–4. IEEE (2013)

    Google Scholar 

  11. Kang, H., Nam, Y., Choi, S.: Composite common spatial pattern for subject-to-subject transfer. IEEE Sig. Process. Lett. 16(8), 683–686 (2009)

    Article  Google Scholar 

  12. Kannathal, N., Choo, M.L., Acharya, U.R., Sadasivan, P.: Entropies for detection of epilepsy in eeg. Comput. Methods Progr. Biomed. 80(3), 187–194 (2005)

    Article  Google Scholar 

  13. Li, Y., Wen, P., et al.: Analysis and classification of eeg signals using a hybrid clustering technique. In: IEEE/ICME International Conference on Complex Medical Engineering (CME), pp. 34–39. IEEE (2010)

    Google Scholar 

  14. Li, Y., Wen, P.P., et al.: Clustering technique-based least square support vector machine for eeg signal classification. Comput. Methods Progr. Biomed. 104(3), 358–372 (2011)

    Article  Google Scholar 

  15. Li, Y., Wen, P.P., et al.: Modified cc-lr algorithm with three diverse feature sets for motor imagery tasks classification in eeg based brain-computer interface. Comput. Methods Progr. Biomed. 113(3), 767–780 (2014)

    Article  MathSciNet  Google Scholar 

  16. Lin, C.J., Hsieh, M.H.: Classification of mental task from eeg data using neural networks based on particle swarm optimization. Neurocomputing 72(4), 1121–1130 (2009)

    Article  Google Scholar 

  17. Lin, Y.P., Wang, C.H., Jung, T.P., Wu, T.L., Jeng, S.K., Duann, J.R., Chen, J.H.: EEG-based emotion recognition in music listening. IEEE Trans. Biomed. Eng. 57(7), 1798–1806 (2010)

    Article  Google Scholar 

  18. Lotte, F., Guan, C.: Spatially regularized common spatial patterns for eeg classification. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 3712–3715. IEEE (2010)

    Google Scholar 

  19. Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: Regularized common spatial patterns with generic learning for eeg signal classification. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, pp. 6599–6602. IEEE (2009)

    Google Scholar 

  20. Millan, J.R.: On the need for on-line learning in brain-computer interfaces. In: Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, vol. 4, pp. 2877–2882. IEEE (2004)

    Google Scholar 

  21. Rankine, L., Stevenson, N., Mesbah, M., Boashash, B.: A nonstationary model of newborn eeg. IEEE Trans. Biomed. Eng. 54(1), 19–28 (2007)

    Article  Google Scholar 

  22. Shadabi, F., Sharma, D.: Artificial intelligence and data mining techniques in medicine-success stories. In: International Conference on BioMedical Engineering and Informatics, BMEI, vol. 1, pp. 235–239. IEEE (2008)

    Google Scholar 

  23. Shen, K.Q., Ong, C.J., Li, X.P., Hui, Z., Wilder-Smith, E.P.: A feature selection method for multilevel mental fatigue eeg classification. IEEE Trans. Biomed. Eng. 54(7), 1231–1237 (2007)

    Article  Google Scholar 

  24. Siuly, S., Li, Y., Wen, P.: EEG signal classification based on simple random sampling technique with least square support vector machine. Int. J. Biomed. Eng. Technol. 7(4), 390–409 (2011)

    Article  Google Scholar 

  25. Siuly, S., Li, Y.: Improving the separability of motor imagery eeg signals using a cross correlation-based least square support vector machine for brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 20(4), 526–538 (2012)

    Article  Google Scholar 

  26. Siuly, S., Li, Y.: Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic eeg signal classification. Comput. Methods Progr. Biomed. 119(1), 29–42 (2015)

    Article  Google Scholar 

  27. Siuly, S., Li, Y.: Discriminating the brain activities for brain-computer interface applications through the optimal allocation-based approach. Neural Comput. Appl. 26(4), 799–811 (2015)

    Article  Google Scholar 

  28. Siuly, S., Li, Y., Zhang, Y.: EEG Signal Analysis and Classification: Techniques and Applications. Springer, Cham (2017)

    Google Scholar 

  29. Siuly, S., Zhang, Y.: Medical big data: neurological diseases diagnosis through medical data analysis. Data Sci. Eng. 1(2), 54–64 (2016)

    Article  Google Scholar 

  30. Stam, C., Montez, T., Jones, B., Rombouts, S., Van Der Made, Y., Pijnenburg, Y., Scheltens, P.: Disturbed fluctuations of resting state eeg synchronization in alzheimer’s disease. Clin. Neurophysiol. 116(3), 708–715 (2005)

    Article  Google Scholar 

  31. Sun, S., Zhang, C.: Adaptive feature extraction for eeg signal classification. Med. Biol. Eng. Compu. 44(10), 931–935 (2006)

    Article  Google Scholar 

  32. Sun, S., Zhang, C., Lu, Y.: The random electrode selection ensemble for eeg signal classification. Pattern Recogn. 41(5), 1663–1675 (2008)

    Article  MATH  Google Scholar 

  33. Sun, S., Zhang, C., Zhang, D.: An experimental evaluation of ensemble methods for eeg signal classification. Pattern Recogn. Lett. 28(15), 2157–2163 (2007)

    Article  Google Scholar 

  34. Supriya, S., Siuly, S., Wang, H., Cao, J., Zhang, Y.: Weighted visibility graph with complex network features in the detection of epilepsy. IEEE Access 4, 6554–6566 (2016)

    Article  Google Scholar 

  35. Wang, H., Xu, J.: Local discriminative spatial patterns for movement-related potentials-based eeg classification. Biomed. Sig. Process. Control 6(4), 427–431 (2011)

    Article  MathSciNet  Google Scholar 

  36. Wang, J., Liu, P., She, M.F., Nahavandi, S., Kouzani, A.: Bag-of-words representation for biomedical time series classification. Biomed. Sig. Process. Control 8(6), 634–644 (2013)

    Article  Google Scholar 

  37. Wu, W., Gao, X., Hong, B., Gao, S.: Classifying single-trial eeg during motor imagery by iterative spatio-spectral patterns learning (isspl). IEEE Trans. Biomed. Eng. 55(6), 1733–1743 (2008)

    Article  Google Scholar 

  38. Yong, X., Ward, R.K., Birch, G.E.: Sparse spatial filter optimization for eeg channel reduction in brain-computer interface. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2008. pp. 417–420. IEEE (2008)

    Google Scholar 

  39. Zarei, R., He, J., Siuly, S., Zhang, Y.: A PCA aided cross-covariance scheme for discriminative feature extraction from eeg signals. Comput. Methods Progr. Biomed. 146, 47–57 (2017)

    Article  Google Scholar 

  40. Zarei, R., He, J., Huang, G., Zhang, Y.: Effective and efficient detection of premature ventricular contractions based on variation of principal directions. Digit. Sig. Process. 50, 93–102 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, Australia

    Siuly Siuly, Roozbeh Zarei, Hua Wang & Yanchun Zhang

Authors
  1. Siuly Siuly
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roozbeh Zarei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanchun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Siuly Siuly .

Editor information

Editors and Affiliations

  1. University of Queensland, Brisbane, Queensland, Australia

    Zi Huang

  2. Nanyang Technological University, Singapore, Singapore

    Xiaokui Xiao

  3. University of New South Wales, Sydney, New South Wales, Australia

    Xin Cao

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Siuly, S., Zarei, R., Wang, H., Zhang, Y. (2017). A New Data Mining Scheme for Analysis of Big Brain Signal Data. In: Huang, Z., Xiao, X., Cao, X. (eds) Databases Theory and Applications. ADC 2017. Lecture Notes in Computer Science(), vol 10538. Springer, Cham. https://doi.org/10.1007/978-3-319-68155-9_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68155-9_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68154-2

  • Online ISBN: 978-3-319-68155-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation