Abstract
Multi-electrode arrays contain an increasing number of electrodes. The manual selection of good quality signals among hundreds of electrodes becomes impracticable for experimental neuroscientists. This increases the need for an automated selection of electrodes containing good quality signals. To motivate the automated selection, three experimenters were asked to assign quality scores, taking one of four possible values, to recordings containing action potentials obtained from the monkey primary somatosensory cortex and the superior parietal lobule. Krippendorff’s alpha-reliability was then used to verify whether the scores, given by different experimenters, were in agreement. A Gaussian process classifier was used to automate the prediction of the signal quality using the scores of the different experimenters. Prediction accuracies of the Gaussian process classifier are about 80% when the quality scores of different experimenters are combined, through a median vote, to train the Gaussian process classifier. It was found that predictions based also on firing rate features are in closer agreement with the experimenters’ assignments than those based on the signal-to-noise ratio alone.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Brown, E.N., Kass, R.E., Mitra, P.P.: Multiple Neural Spike Train Data Analysis: State-of-the-Art and Future Challenges. Nat. Neurosci. 7, 456–461 (2004)
Pine, J.: A History of MEA Development. In: Taketani, M., Baudry, M. (eds.) Advances in Network Electrophysiology: Using Multi-Electrode Arrays, pp. 3–23. Springer-Verlag New York Inc., New York (2006)
Seidl, K., Torfs, T., De Mazière, P.A., Van Dijck, G., Csercsa, R., Dombovari, B., et al.: Control and Data Acquisition Software for High-density CMOS-based Microprobe Arrays Implementing Electronic Depth Control. Biomed. Tech. 55, 183–191 (2010)
Sato, T., Suzukia, T., Mabuchi, K.: A New Multi-electrode Array Design for Chronic Neural Recording, with Independent and Automatic Hydraulic Positioning. J. Neurosci. Methods 160, 45–51 (2007)
Fee, M.S., Leonardo, A.: Miniature Motorized Microdrive and Commutator System for Chronic Neural Recording in Small Animals. J. Neurosci. Methods 112, 83–94 (2001)
Cham, J.G., Branchaud, E.A., Nenadic, Z., Greger, B., Andersen, R.A., Burdick, J.W.: Semi-chronic Motorized Microdrive and Control Algorithm for Autonomously Isolating and Maintaining Optimal Extracellular Action Potentials. J. Neurophysiol. 93, 570–579 (2005)
Jackson, N., Sridharan, A., Anand, S., Baker, M., Okandan, M., Muthuswamy, J.: Long-term Neural Recordings using MEMS based Movable Microelectrodes in the Brain. Front. Neuroeng. 3(10), 1–10 (2010)
Girolami, M., Rogers, S.: Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. Neural Comput. 18, 1790–1817 (2006)
Herwik, S., Kisban, S., Aarts, A., Seidl, K., Girardeau, G., Benchenane, K., et al.: Fabrication Technology for Silicon Based Microprobe Arrays used in Acute and Subchronic Neural Recording. J. Micromech. Microeng. 19, 074008 (2009)
Krippendorff, K.: Content Analysis: an Introduction to its Methodology. Sage Publications Inc., California (2004)
Nenadic, Z., Burdick, J.W.: Spike Detection Using the Continuous Wavelet Transform. IEEE Trans. Biomed. Eng. 52, 74–87 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Van Dijck, G. et al. (2010). Toward Automated Electrode Selection in the Electronic Depth Control Strategy for Multi-unit Recordings. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-17534-3_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17533-6
Online ISBN: 978-3-642-17534-3
eBook Packages: Computer ScienceComputer Science (R0)