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EEG Spikes Detection, Sorting, and Localization

Authors: Mazin Z. Othman, Maan M. Shaker, Mohammed F. Abdullah


This study introduces a new method for detecting, sorting, and localizing spikes from multiunit EEG recordings. The method combines the wavelet transform, which localizes distinctive spike features, with Super-Paramagnetic Clustering (SPC) algorithm, which allows automatic classification of the data without assumptions such as low variance or Gaussian distributions. Moreover, the method is capable of setting amplitude thresholds for spike detection. The method makes use of several real EEG data sets, and accordingly the spikes are detected, clustered and their times were detected.

Keywords: EEG time localizations, EEG spike detection, superparamagnetic algorithm, wavelet transform.

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[1] M. Lewicki, "A review of methods for spike sorting: the detection and classification of neural action potentials", Network: Comput. Neural Syst., No. 9, pp: (R53-R78), 1998.
[2] M. Abeles, and Goldstein M., "Mutispike Train Analysis", Proc. IEEE, No. 65, pp. 762-773, 1977.
[3] R. Quian Quiroga, Z. Nadasdy, and Y. Ben-Shaul, "Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering", Neural Computation, No. 16, pp. 1661-1687, 2004.
[4] M. Misiti, Y. Misiti, G. Oppenheim, & J. Poggi, "Wavelet toolbox user-s guide", Ver. 2.2, The MathWorks, Inc., 2002.
[5] A. Graps, "Introduction to wavelets", (Original paper published by the IEEE Computer Society (1995) Vol. 2 No. 2), 2003.
[6] R. Quian Quiroga, O. A. Rosso, E. Başar, & M. Schürmann, "Wavelet entropy in event-related potential: a new method shows ordering of eeg oscillations", Biol. Cybern., No. 84, pp. 291-299, 2001.
[7] K. Smirnov, "Kolmogorov-Smirnov Test", available, 2004.
[8] M. Blatt, Wiseman S., & Domany E., "Super-paramagnetic clustering of data". Phys. Rev. Lett., No. 76, pp. 3251-3254, 1996.