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Infant Auditory Processing and Event-related Brain Oscillations

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

CARLA: Adjusted common average referencing for cortico-cortical evoked potential data.

Harvey Huang¹, Gabriela Ojeda Valencia², Nicholas M Gregg³

¹Mayo Clinic Medical Scientist Training Program, Rochester, MN, USA.

Journal of Neuroscience Methods

|May 6, 2024

Summary

CARLA, a new adaptive algorithm, improves human brain connectivity mapping by reducing noise in intracranial EEG measurements. This method minimizes bias in cortico-cortical evoked potentials (CCEPs) for more accurate analysis.

Keywords:

Common average reference Cortico-cortical evoked potential Intracranial EEG Re-referencing Single pulse electrical stimulation Stereo EEG

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Area of Science:

Neuroscience
Computational Neuroscience
Biomedical Engineering

Background:

Human brain connectivity mapping relies on intracranial EEG and electrical stimulation.
Raw cortico-cortical evoked potentials (CCEPs) are susceptible to noise, impacting analysis accuracy.
Standard Common Average Referencing (CAR) can introduce bias by including responsive channels in the average.

Purpose of the Study:

To introduce and validate a novel adaptive Common Average Referencing (CAR) algorithm, CAR by Least Anticorrelation (CARLA).
To minimize bias in CCEPs by adaptively selecting non-responsive channels for re-referencing.
To improve the signal quality and accuracy of human brain connectivity mapping.

Main Methods:

CARLA algorithm development: Channels are ordered by cross-trial covariance and iteratively added to the common average.
CARLA validation using simulated CCEP data with varying numbers of responsive channels.
CARLA evaluation on real CCEP data from four human participants, assessing signal quality via inter-channel dependency (mean R²).

Main Results:

CARLA demonstrated high specificity and sensitivity on simulated data, with minimal erroneous inclusion of responsive or exclusion of unresponsive channels.
On real data, CARLA re-referencing significantly reduced inter-channel dependency (mean R²) compared to standard CAR and no re-referencing.
CARLA effectively minimized bias by adaptively selecting an optimal subset of non-responsive channels.

Conclusions:

CARLA offers a robust solution for reducing noise and bias in CCEP recordings.
The adaptive nature of CARLA enhances the reliability of human brain connectivity mapping.
CARLA represents a significant advancement over traditional referencing techniques for intracranial EEG analysis.