Real-time signal processing for high-density microelectrode array systems

Authors

  • K. Imfeld,

    Corresponding author
    1. Institute of Microtechnology (IMT), Université de Neuchâtel, Jaquet-Droz 1, 2007 Neuchâtel, Switzerland
    • Institute of Microtechnology (IMT), Université de Neuchâtel, Jaquet-Droz 1, 2007 Neuchâtel, Switzerland
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  • A. Maccione,

    1. Neuroengineering and Bio-nano Technology Group, Department of Biophysical and Electronic Engineering (DIBE), University of Genova, Via Opera Pia 11a, 16145 Genova, Italy
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  • M. Gandolfo,

    1. Neuroengineering and Bio-nano Technology Group, Department of Biophysical and Electronic Engineering (DIBE), University of Genova, Via Opera Pia 11a, 16145 Genova, Italy
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  • S. Martinoia,

    1. Neuroengineering and Bio-nano Technology Group, Department of Biophysical and Electronic Engineering (DIBE), University of Genova, Via Opera Pia 11a, 16145 Genova, Italy
    2. Department of Neuroscience and Brain Technology, Italian Institute of Technology, Via Morego 30, 16163 Genova, Italy
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  • P.-A. Farine,

    1. Institute of Microtechnology (IMT), Université de Neuchâtel, Jaquet-Droz 1, 2007 Neuchâtel, Switzerland
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  • M. Koudelka-Hep,

    1. Institute of Microtechnology (IMT), Université de Neuchâtel, Jaquet-Droz 1, 2007 Neuchâtel, Switzerland
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  • L. Berdondini

    1. Department of Neuroscience and Brain Technology, Italian Institute of Technology, Via Morego 30, 16163 Genova, Italy
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Abstract

The microelectrode array (MEA) technology is continuously progressing towards higher integration of an increasing number of electrodes. The ensuing data streams that can be of several hundreds or thousands of Megabits/s require the implementation of new signal processing and data handling methodologies to substitute the currently used off-line analysis methods. Here, we present one approach based on the hardware implementation of a wavelet-based solution for real-time processing of extracellular neuronal signals acquired on high-density MEAs. We demonstrate that simple mathematical operations on the discrete wavelet transform (DWT) coefficients can be used for efficient neuronal spike detection and sorting. As the DWT is particularly well suited for implementation on dedicated hardware, we elaborated a wavelet processor on a field programmable gate array (FPGA) in order to compute the wavelet coefficients on 256 channels in real-time. By providing sufficient hardware resources, this solution can be easily scaled up for processing more electrode channels. Copyright © 2008 John Wiley & Sons, Ltd.

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