EEG Spikes Detection, Sorting, and Localization

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.

Authors:

• Mazin Z. Othman
• Maan M. Shaker
• Mohammed F. Abdullah

Keywords:

• EEG time localizations
• EEG spike detection
• superparamagnetic algorithm
• wavelet transform.

References:

[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
http://www.physics.csbsju.edu/stats/KStest.html, 2004.
[8] M. Blatt, Wiseman S., & Domany E., "Super-paramagnetic clustering of
data". Phys. Rev. Lett., No. 76, pp. 3251-3254, 1996.