Abstract
Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with interelectrode distances as small as 30 µm. So far, neuroscientists manually select a subset of those electrodes depending on their appraisal of the “usefulness” of the recorded signals, which makes the process subjective but more importantly too time consuming to be useable in practice. The ever-increasing number of recording electrodes on microelectrode probes calls for an automated selection of electrodes containing “good quality signals” or “signals of interest.” This article reviews the different criteria for electrode selection as well as the basic signal processing steps to prepare the data to compute those criteria. We discuss three of them. The first two select the electrodes based on “signal quality.” The first criterion computes the penalized signal-to-noise ratio (SNR); the second criterion models the neuroscientist’s appraisal of signal quality. Last, our most recent work allows the selection of electrodes that capture particular anatomical cell types. The discussed algorithms perform what is called in the literature “electronic depth control” in contrast to the mechanical repositioning of the electrode shafts in search of “good quality signals” or “signals of interest.”
Acknowledgments
Gert Van Dijck is supported by the Michael and Morven Heller Research Fellowship in Computing Science at St Catharine’s College, University of Cambridge. Marc Van Hulle is supported by the CREA (CREA/07/027) and the Financing program (PFV/10/008) of the K.U. Leuven, the Belgian Fund for Scientific Research – Flanders (G.0588.09), the Interuniversity Attraction Poles Programme (IUAP P7/21), the Flemish Regional Ministry of Education (Belgium) (GOA 10/019), the Flemish Agency for Innovation by Science and Technology (TETRA project Spellbinder).
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©2014 by De Gruyter
Artikel in diesem Heft
- Frontmatter
- Editorial
- Neural probes – microsystems to interface with the brain
- Special issue articles
- Application of floating silicon-based linear multielectrode arrays for acute recording of single neuron activity in awake behaving monkeys
- In vivo validation of the electronic depth control probes
- Approaches for drug delivery with intracortical probes
- Multisite monitoring of choline using biosensor microprobe arrays in combination with CMOS circuitry
- Influence of bio-coatings on the recording performance of neural electrodes
- Review
- Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays
- Research articles
- Non-invasive determination of respiratory effort in spontaneous breathing and support ventilation: a validation study with healthy volunteers
- Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure
- Automated detection of circinate exudates in retina digital images using empirical mode decomposition and the entropy and uniformity of the intrinsic mode functions
Artikel in diesem Heft
- Frontmatter
- Editorial
- Neural probes – microsystems to interface with the brain
- Special issue articles
- Application of floating silicon-based linear multielectrode arrays for acute recording of single neuron activity in awake behaving monkeys
- In vivo validation of the electronic depth control probes
- Approaches for drug delivery with intracortical probes
- Multisite monitoring of choline using biosensor microprobe arrays in combination with CMOS circuitry
- Influence of bio-coatings on the recording performance of neural electrodes
- Review
- Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays
- Research articles
- Non-invasive determination of respiratory effort in spontaneous breathing and support ventilation: a validation study with healthy volunteers
- Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure
- Automated detection of circinate exudates in retina digital images using empirical mode decomposition and the entropy and uniformity of the intrinsic mode functions