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Adaptive common average reference for in vivo multichannel local field potentials.

Liu Xinyu1, Wan Hong1,2, Li Shan1

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|January 4, 2019
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Summary

A new adaptive common average reference (ACAR) method effectively removes artifacts from in vivo neural recordings. This technique improves local field potential (LFP) signal quality, especially in low signal-to-noise ratio environments.

Keywords:
Adaptive noise cancelingCommon average referenceLocal field potentialMicroelectrode arraySpatially correlated artifacts

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Bioengineering

Background:

  • In vivo neural recordings, particularly local field potentials (LFPs), are susceptible to spatially correlated artifacts.
  • These artifacts are especially problematic in awake and behaving subjects, compromising data integrity.
  • Existing artifact removal methods may not be sufficiently effective, particularly in low signal-to-noise ratio (SNR) conditions.

Purpose of the Study:

  • To introduce and evaluate a novel artifact removal technique for in vivo neural recordings.
  • To enhance the quality of local field potential (LFP) data by mitigating spatially correlated artifacts.
  • To provide a robust pre-processing step for LFP analysis in neuroscience research.

Main Methods:

  • Developed an adaptive common average reference (ACAR) method, inspired by adaptive noise canceling (ANC).
  • ACAR utilizes correlative features of noise sources and common average referencing (CAR) for artifact removal.
  • Incorporated a correlation analysis for automatic channel selection prior to CAR reference generation.

Main Results:

  • ACAR demonstrated superior performance in removing spatially correlated artifacts compared to standard CAR and other existing methods.
  • The method proved effective on both synthesized and real neural data from pigeon hippocampus.
  • ACAR showed particular efficacy in improving signal quality under low signal-to-noise ratio (SNR) conditions.

Conclusions:

  • The adaptive common average reference (ACAR) method is a highly effective technique for removing artifacts in in vivo neural recordings.
  • ACAR offers significant advantages over existing methods, especially for local field potential (LFP) data with low SNR.
  • This method represents a valuable pre-processing tool for advancing in vivo LFP data analysis and interpretation.