PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing

Summary

This protocol presents PIPEMAT-RS, a standardized MATLAB-based preprocessing pipeline for resting-state EEG data. It ensures artifact removal, improves signal quality, and enhances data reproducibility across studies. The pipeline automates key preprocessing steps, including filtering, independent component analysis (ICA), and artifact classification, facilitating consistent and reliable EEG analysis for neurophysiological research.

Abstract

Electroencephalography (EEG) is a crucial tool in neuroscience research and clinical applications, but raw EEG data often contain noise and artifacts that compromise signal quality. To address this, we developed PIPEMAT-RS, a standardized MATLAB-based preprocessing pipeline for resting-state EEG data. PIPEMAT-RS follows a structured seven-step workflow: file format conversion, EEG montage configuration, downsampling, filtering, artifact rejection, independent component analysis (ICA), and ICLabel classification for automated artifact removal. This protocol enhances EEG data quality by minimizing human intervention while maintaining high accuracy in artifact rejection. It was validated using multiple datasets, demonstrating its robustness in improving signal integrity. PIPEMAT-RS provides a systematic approach that facilitates reproducibility and reliability in EEG studies, aligning with commonly adopted practices in the field and offering a clearly documented structure that can complement existing pipelines. By standardizing EEG preprocessing, PIPEMAT-RS facilitates neurophysiological research and clinical applications, allowing for more accurate interpretations of resting-state brain activity and its associations with neurological and psychiatric conditions.

Introduction

The electroencephalogram (EEG) is a critical tool in both neuroscience research and clinical practice, offering valuable insights into brain electrical activity. Resting-state EEG data, in particular, are extensively utilized to investigate various neurological and psychiatric conditions, such as stroke, fibromyalgia, and chronic neuropathic pain. Despite its widespread application, the analysis of EEG data necessitates rigorous preprocessing to eliminate artifacts and noise, ensuring the integrity and reliability of the results^1,2,3. Over the years, EEG has been invaluable i....

Protocol

PIPEMAT-RS was developed as part of our ongoing research project, approved by the Ethics Committee of the Clinics Hospital, University of São Paulo Medical School (CAAE: 86832518.7.0000.0068). This project aims to enhance the quality and reliability of resting-state EEG data for various neurological and psychiatric studies. The data used in this study are derived from a cohort study, the protocol of which was previously published by Simis et al. (2021)³¹. The main study is still in progress, with data collection ongoing for the remaining clinical groups. The script for the pipeline is provided in Supplementary File 1.

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Results

The validation of the EEG preprocessing pipeline is essential to ensure the reliability and effectiveness of the methods employed for extracting meaningful neurophysiological data. This process involves empirical evidence from published studies, statistical assessments, and comparisons with established benchmarks in the field.

The robustness and utility of PIPEMAT-RS have been demonstrated through its successful application in several published studies spanning a range of neurological and psyc.......

Discussion

The PIPEMAT-RS pipeline was developed to provide a standardized, efficient method for preprocessing resting-state EEG data. Critical steps in this protocol include artifact rejection and Independent Component Analysis (ICA), both of which significantly enhance the signal-to-noise ratio and ensure the extraction of meaningful neural signals. The combination of automatic artifact rejection using the clean_rawdata function and manual inspection via eegplot ensures comprehensive artifact management, balancing efficiency and .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
MATLAB	MathWorks	R2020a or newer	Required for executing the PIPEMAT-RS script.
EEG Recording System	Brain Products	https://www.brainproducts.com/	EEG acquisition system used for data collection. Other brands  include BioSemi / ANT Neuro (or equivalent)
EEGLAB Toolbox	Swartz Center for Computational Neuroscience	N/A	Open-source MATLAB toolbox for EEG analysis.
Electrodes (Ag/AgCl)	Brain Products	https://www.brainproducts.com/solutions/r-net/	Used in EEG data acquisition. Other brands  include BioSemi / ANT Neuro (or equivalent)
ICLabel Plugin	Swartz Center for Computational Neuroscience	N/A	Automated artifact classification tool for EEG.
PIPEMAT-RS Script	N/A	N/A	Custom MATLAB script for standardized EEG preprocessing.
Signal Amplifier	Brain Products	https://www.brainproducts.com/solutions/#amplifiers	Amplifies EEG signals for processing. Other brands  include BioSemi / ANT Neuro (or equivalent)
Standard 64-Channel EEG Cap	Brain Products	https://www.brainproducts.com/solutions/#electrodes-caps	Electrode cap for EEG recording. Other brands  include BioSemi / ANT Neuro (or equivalent)