Abstract
When analyzing the neurobiological data many of its aspects have to be carefully looked upon. Data coming from the MRI, EMG or microrecording all have its special properties that have to be extracted during the process analysis. In case of recordings coming from the microrecording procedure i.e. from microelectrodes placed within the neuronal tissue signal can be analyzed in at least two ways. First approach focuses on the background noise present in such recordings. Second one, looks upon the presence of the spikes - electrical signs of the bioelectrical neurophysiological activity of neuron cells. In a given recording one may often find many spikes with different shapes. For further analytical reasons it is frequently desired that spikes are to be grouped according to their shape. Such grouping / shape clustering is called spike sorting and there are many known approaches to that problem. Still, before spikes are detected and sorted the raw recorded signal is almost always filtered and altered in various DSP processes. This preliminary DSP operations may significantly hamper the spike sorting efficiency. Analysis presented in this paper provides answer as to which frequency bands are alone sufficient for proper and successful spike sorting.
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References
Jensen, A., Cour-Harbo, A.I.: Ripples in Mathematics. Springer (2001)
Smith, S.W.: Digital Signal Processing. Elsevier (2003)
Nolte, J.: The Human Brain, An Introduction to Its Functional Anatomy (2009)
Israel, Z., Burchiel, K.J.: Microelectrode Recording in Movement Disorder Surgery. Thieme Medical Publishers (2004)
Archer, C., Hochstenbach, M.E., Hoede, C., et al.: Neural spike sorting with spatio-temporal features. In: In:Proceedings of the 63rd European Study Group Mathematics with Industry, January 28 - February 1 (2008)
Quian Quiroga, R., Nadasdy, Z., Ben-Shaul, Y.: Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering. MIT Press (2004)
Lewicki, M.S.: A review of methods for spike sorting: The detection and classification of neural action potentials. 9(4), R53–R78 (1998)
Reilly, C., Wang, C., Rutherford, M.: A rapid method for the comparison of cluster analyses. Statistica Sinica 15, 19–33 (2005)
Cohen, J.: A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20(1), 37–46 (1960)
Seigel, D.G., Podgo, M.J., Remaley, N.A.: Acceptable values of kappa for comparison of two groups. American Journal of Epidemiology 135(5), 571–578 (2012)
Kaneko, H., Suzuki, S.S., Okada, J., Akamatsu, M.: Multineuronal spike classification based on multisite electrode recording, whole-waveform analysis, and hierarchical clustering. IEEE Transactions on Biomedical Engineering 46(3) (1999)
Fleiss, J.L.: Measuring nominal scale agreement among many raters. Psychological Bulletin, Vol 76(5), 378–382 (1971)
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Ciecierski, K., Raś, Z.W., Przybyszewski, A.W. (2014). Spike Sorting Based upon PCA over DWT Frequency Band Selection. In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_16
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DOI: https://doi.org/10.1007/978-3-319-08326-1_16
Publisher Name: Springer, Cham
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