Abstract
Spike sorting for neuron recordings is one of the core tasks in brain function studies. Spike sorting always consists of spike detection, feature extraction and clustering. Most of the clustering algorithms adopted in spike sorting schemes are subject to the shapes and structures of the signal except the spectral clustering algorithm. To improve the performance of spectral clustering algorithm for spike sorting, in this paper, a locally weighted co-association matrix is employed as the similarity matrix and the Shannon entropy is also introduced to measure the dependability of clustering. Experimental results show that the performance of spike sorting with the improved spectral clustering algorithm is superior to that of spike sorting with other classic clustering algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Quian Quiroga, R.: Spike sorting. Curr. Biol. 22(2), R45 (2012)
Takahashi, S., Anzai, Y., Sakurai, Y.: Automatic sorting for multi-neuronal activity recorded with tetrodes in the presence of overlapping spikes. J. Neurophysiol. 89(4), 2245–2258 (2003)
Zhang, P.M., et al.: Spike sorting based on automatic template reconstruction with a partial solution to the overlapping problem. J. Neurosci. Methods 135(1–2), 55–65 (2004)
Borghi, T., et al.: A simple method for efficient spike detection in multiunit recordings. J. Neurosci. Methods 163(1), 176–180 (2007)
Shoham, S., Fellows, M.R., Normann, R.A.: Robust, automatic spike sorting using mixtures of multivariate t-distributions. J. Neurosci. Methods 127(2), 111–122 (2003)
Hulata, E., Segev, R., Ben-Jacob, E.: A method for spike sorting and detection based on wavelet packets and Shannon’s mutual information. J. Neurosci. Methods 117(1), 1–12 (2002)
Takekawa, T., Isomura, Y., Fukai, T.: Accurate spike sorting for multi-unit recordings. Eur. J. Neurosci. 31(2), 263–272 (2010)
Wood, F., Black, M.J.: A nonparametric Bayesian alternative to spike sorting. J. Neurosci. Methods 173(1), 1–12 (2008)
Jia, H.J., et al.: The latest research progress on spectral clustering. Neural Comput. Appl. 24(7–8), 1477–1486 (2014)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. Adv. Neural. Inf. Process. Syst. 2, 849–856 (2001)
Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: Advances in Neural Information Processing Systems (2004)
Übeyli, E.D.: Wavelet/mixture of experts network structure for EEG signals classification. Expert Syst. Appl. 34(3), 1954–1962 (2008)
Quiroga, R.Q., Nadasdy, Z., Ben-Shaul, Y.: Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 16(8), 1661–1687 (2004)
Quian, Q.R., et al.: Wavelet transform in the analysis of the frequency composition of evoked potentials. Brain Res. Protoc. 8(1), 16–24 (2001)
Adeli, H., Ghosh-Dastidar, S., Dadmehr, N.: A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Trans. Biomed. Eng. 54(2), 205–211 (2007)
Fahmy, A.A., et al.: Feature extraction of epilepsy EEG using discrete wavelet transform. In: Computer Engineering Conference (2017)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
Fred, A.L., Jain, A.K.: Combining multiple clustering using evidence accumulation. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 835–850 (2005)
Huang, D., Wang, C.D., Lai, J.H.: Locally weighted ensemble clustering. IEEE Trans. Cybern. 48(5), 1460–1473 (2018)
Zhou, Z.H., Tang, W.: Cluster ensemble. Knowl.-Based Syst. 19(1), 77–83 (2006)
Acknowledgement
This work was partially supported by Natural Science Foundation of China (No. 61603197, 61772284, 61876091).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ji, W., Li, Z., Li, Y. (2019). Spike Sorting with Locally Weighted Co-association Matrix-Based Spectral Clustering. In: U., L., Lauw, H. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11607. Springer, Cham. https://doi.org/10.1007/978-3-030-26142-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-26142-9_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26141-2
Online ISBN: 978-3-030-26142-9
eBook Packages: Computer ScienceComputer Science (R0)