Related Experiment Video
Updated: Jun 28, 2025
09:42
Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
Published on: May 12, 2019
6.0K
Data processing techniques impact quantification of cortico-cortical evoked potentials
L H Levinson1, S Sun2, C J Paschall2
1University of Washington Graduate Program in Neuroscience, 1959 NE Pacific Street, T-47, Seattle, WA 98195-7270, United States.
Journal of Neuroscience Methods
|April 23, 2024
Summary
Processing electrophysiology data, like cortico-cortical evoked potentials (CCEPs), significantly impacts results. Careful application of filters and re-referencing is crucial for accurate quantification of CCEP connectivity.
Area of Science:
- Neuroscience
- Electrophysiology
Background:
- Cortico-cortical evoked potentials (CCEPs) are vital for studying human brain connectivity.
- CCEP data is susceptible to noise, necessitating preprocessing.
- Current preprocessing strategies vary across studies.
Purpose of the Study:
- To systematically evaluate the impact of common preprocessing techniques on CCEP quantification.
- To compare the effects of filtering and re-referencing on CCEP data.
Main Methods:
- Systematic comparison of common average re-referencing and various filtering techniques.
- Analysis of CCEP magnitude and morphology changes due to preprocessing.
Main Results:
- Common average re-referencing and aggressive filtering significantly alter CCEP quantification.
- Filtering is more effective than re-referencing or trial averaging in noise reduction.
- Specific filter cutoffs (>0.5 Hz high-pass, <200 Hz low-pass) and common average re-referencing notably impact results.
Conclusions:
- Existing CCEP processing methods require careful application to balance noise reduction and data integrity.
- Reporting preprocessing methods, especially re-referencing, is essential.
- A framework for selecting appropriate CCEP processing pipelines based on noise levels is proposed, favoring minimal filtering.
