Spike sorting based on multi-class support vector machine with superposition resolution | Medical & Biological Engineering & Computing Skip to main content
Springer Nature Link
Log in

Spike sorting based on multi-class support vector machine with superposition resolution

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

A new spike sorting method based on the support vector machine (SVM) is proposed to resolve the superposition problem. The spike superposition is generally resolved by the template matching. Previous template matching methods separate the spikes through linear classifiers. The classification performance is severely influenced by the background noise included in spike trains. The nonlinear classifiers with high generation ability are required to deal with the task. A multi-class SVM classifier is therefore applied to separate the spikes, which contains several binary SVM classifiers. Every binary SVM classifier corresponding to one spike class is used to identify the single and superposition spikes. The superposition spikes are decomposed through template extraction. The experimental results on the simulated and real data demonstrate the utility of the proposed method.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Argoud FI, De Azevedo FM, Neto JM, Grillo E (2006) SADE3: an effective system for automated detection of epileptiform events in long-term EEG based on context information. Med Biol Eng Comput 44(6):459–470

    Article  Google Scholar 

  2. Bankman IN, Johnson KO, Schneider W (1993) Optimal detection, classification, and superposition resolution in neural waveform recordings. IEEE Trans Biomed Eng 40(8):836–841

    Article  Google Scholar 

  3. Boostani R, Graimann B, Moradi MH, Pfurtscheller G (2007) A comparison approach toward finding the best feature and classifier in cue-based BCI. Med Biol Eng Comput 45:403–412

    Article  Google Scholar 

  4. Chandra R, Optican LM (1997) Detection, classification, and superposition resolution of action potentials in multiunit single-channel recordings by an on-line real-time neural network. IEEE Trans Biomed Eng 44:403–412

    Article  Google Scholar 

  5. Chen AH, Zhou Y, Gong HQ, Liang PJ (2003) Chicken retinal ganglion cells response characteristics: multi-channel electrode recording study. Sci China Ser C 33:82–88

    Google Scholar 

  6. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, Cambridge

    Google Scholar 

  7. Fee MS, Mitra PP, Kleinfeld D (1996) Variability of extracellular spike waveforms of cortical neurons. J Neurophysiol 76:3823–3833

    Google Scholar 

  8. Harris KD, Henze DA, Csicsvari J, Hirase H (2000) Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J Neurophysiol 84:401–414

    Google Scholar 

  9. Hsu CW, Lin CJ (2002) A comparison on methods for multi-class support vector machines. IEEE Trans Neural Netw 13:415–425

    Article  Google Scholar 

  10. Kim KH, Kim SJ (2000) Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier. IEEE Trans Biomed Eng 47:1406–1411

    Article  Google Scholar 

  11. Kim KH, Kim SS, Kim SJ (2006) Improvement of spike train decoder under spike detection and classification errors using support vector machine. Med Biol Eng Comput 44:124–130

    Article  Google Scholar 

  12. Lewicki MS (1994) Bayesian modeling and classification of neural signals. Neural Comput 6:1005–1030

    Article  MATH  Google Scholar 

  13. Lewicki MS (1998) A review of methods for spike sorting: the detection and classification of neural action potentials. Netw Comput Neural Syst 9:53–78

    Article  Google Scholar 

  14. Schmidt EM (1984) Computer separation of multi-unit neuroelectric data: a review. J Neurosci Methods 12:95–111

    Article  Google Scholar 

  15. Shoham S, Fellows MR, Normamn R (2003) A robust, automatic spike sorting using mixtures of multivariate t-distributions. J Neurosci Methods 127:111–122

    Article  Google Scholar 

  16. Sun S, Zhang C (2006) Adaptive feature extraction for EEG signal classification. Med Biol Eng Comput 44: 931–935

    Article  MathSciNet  Google Scholar 

  17. Takahashi S, Anzai Y, Sakurai Y (2003) Automatic sorting for multi-neuronal activity recorded with tetrodes in the presence of overlapping spikes. J Neurophysiol 89:2245–2258

    Article  Google Scholar 

  18. Takahashi S, Sakurai Y (2005) Real-time and automatic sorting of multi-neuronal activity for sub-millisecond interactions in vivo. Neuroscience 134:301–315

    Article  Google Scholar 

  19. Takahashi S, Sakurai Y, Tsukada M, Anzai Y (2002) Classification of neuronal activities from tetrode recordings using independent component analysis. Neurocomputing 49:289–298

    Article  Google Scholar 

  20. Vogelstein RJ, Murari K, Thakur PH, Cauwenberghs G, Chakrabartty S, Diehl C (2004) Spike sorting with support vector machines. In: Proceedings of 26th annual international conference on IEEE engineering in medicine and biology society

  21. Wang GL, Zhou Y, Chen AH, Zhang PM, Liang PJ (2006) A robust method for spike sorting with automatic overlap decomposition. IEEE Trans Biomed Eng 53:1195–1198

    Article  Google Scholar 

  22. Zhang PM, Wu JY, Zhou Y, Liang PJ, Yuan JQ (2004) Spike sorting based on automatic template reconstruction with a partial solution to the overlapping problem. J Neurosci Methods 135:55–65

    Article  Google Scholar 

  23. Zouridaks G, Tam DC (2000) Identification of reliable spike templates in multi-unit extracellular recording using fuzzy clustering. Comput Methods Programs Biomed 61:91–98

    Article  Google Scholar 

Download references

Acknowledgments

This study is supported by the National Natural Science Foundation of China (Grant No. 60574038) and the Specialized Research Fund for the Doctoral Program of Higher Education China (Grant No.20060248015).

Author information

Authors and Affiliations

  1. Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, People’s Republic of China

    Weidong Ding & Jingqi Yuan

Authors
  1. Weidong Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jingqi Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jingqi Yuan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ding, W., Yuan, J. Spike sorting based on multi-class support vector machine with superposition resolution. Med Bio Eng Comput 46, 139–145 (2008). https://doi.org/10.1007/s11517-007-0248-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-007-0248-0

Keywords

Search

Navigation