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Time dependence of stimulation/recording-artifact transfer function estimates for neural interface systems.

Nick Chernyy1, Steven J Schiff, Bruce J Gluckman

  • 1Engineering Science and Mechanics Department, The Pennsylvania State University, State College, PA 16802 USA. nchernyy@psu.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
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Stimulus artifacts in neural recordings can be modeled and characterized. Tracking slow changes in these artifacts is crucial for reliable continuous feedback neural control systems.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Control Systems

Background:

  • Continuous feedback control in neural systems necessitates simultaneous state measurement and control output generation.
  • Electrical stimulation in neural interfaces introduces stimulus artifacts that corrupt neural recordings.
  • These artifacts manifest as common mode and differential potentials, confounding underlying neural activity detection.

Purpose of the Study:

  • To model stimulus artifacts as a linearly filtered version of applied electrical current.
  • To develop and demonstrate a method for determining artifact filter properties.
  • To investigate the temporal stability of these artifact properties in chronic recordings.

Main Methods:

  • Modeling stimulus artifacts using linear filtering of electrical current.

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  • Employing multi-taper techniques to determine filter properties.
  • Utilizing data from chronically implanted animals stimulated with polarizing low-frequency electric fields (PLEF).

    • Main Results:

    • Demonstrated a method to characterize stimulus artifact filter properties.
    • Observed slow variations (up to 50% in transfer function magnitude) in artifact properties during chronic recordings.
    • Attributed these variations to changes in tissue bulk impedance and electrode interface properties.

    Conclusions:

    • Stimulus artifacts exhibit dynamic changes over time in chronic neural recording scenarios.
    • These temporal variations in artifact properties must be monitored and compensated for.
    • Accommodating these changes is essential for the success of chronic continuous feedback neural control systems.