A Comprehensive Review of EEGLAB for EEG Signal Processing: Prospects and Limitations

Yuri Pamungkas; Rahmah Yasinta Rangkuti; Evi Triandini; Kanittha Nakkliang; Wawan Yunanto; Muhammad Nur Afnan Uda; Uda Hashim

doi:10.18196/jrc.v6i4.27084

Authors

Yuri Pamungkas Institut Teknologi Sepuluh Nopember
Rahmah Yasinta Rangkuti Institut Teknologi Sepuluh Nopember
Evi Triandini Institut Teknologi dan Bisnis STIKOM Bali
Kanittha Nakkliang Valaya Alongkorn Rajabhat University
Wawan Yunanto National Taiwan University of Science and Technology (NTUST)
Muhammad Nur Afnan Uda Universiti Malaysia Sabah
Uda Hashim Universiti Malaysia Sabah

DOI:

https://doi.org/10.18196/jrc.v6i4.27084

Keywords:

EEGLAB, EEG Signal Processing, ICA, Artifact Removal, EEG Connectivity Analysis

Abstract

EEGLAB is a MATLAB-based software that is widely used for EEG signal processing due to its complete features, analysis flexibility, and active open-source community. This review aims to evaluate the use of EEGLAB based on 55 research articles published between 2020 and 2024, and analyze its prospects and limitations in EEG processing. The articles were obtained from reputable databases, namely ScienceDirect, IEEE Xplore, SpringerLink, PubMed, Taylor & Francis, and Emerald Insight, and have gone through a strict study selection stage based on eligibility criteria, topic relevance, and methodological quality. The review results show that EEGLAB is widely used for EEG data preprocessing such as filtering, ICA, artifact removal, and advanced analysis such as ERP, ERSP, brain connectivity, and activity source estimation. EEGLAB has bright prospects in the development of neuroinformatics technology, machine learning integration, multimodal analysis, and large-scale EEG analysis which is increasingly needed. However, EEGLAB still has significant limitations, including a high reliance on manual inspection in preprocessing, low spatial resolution in source modeling, limited multimodal integration, low computational efficiency for large-scale EEG data, and a high learning curve for new users. To overcome these limitations, future research is recommended to focus on developing more accurate automation methods, increasing the spatial resolution of source analysis, more efficient multimodal integration, high computational support, and implementing open science with a standardized EEG data format. This review provides a novel contribution by systematically mapping EEGLAB’s usage trends and pinpointing critical technical and methodological gaps that must be addressed for broader neurotechnology adoption.

Author Biographies

Yuri Pamungkas, Institut Teknologi Sepuluh Nopember

Department of Medical Technology

Rahmah Yasinta Rangkuti, Institut Teknologi Sepuluh Nopember

Department of Medicine

Evi Triandini, Institut Teknologi dan Bisnis STIKOM Bali

Institut Teknologi dan Bisnis STIKOM Bali

Kanittha Nakkliang, Valaya Alongkorn Rajabhat University

Department of Health System Management

Wawan Yunanto, National Taiwan University of Science and Technology (NTUST)

Department of Computer Science and Information Engineering

Muhammad Nur Afnan Uda, Universiti Malaysia Sabah

Department of Electronic and Computer Engineering

Uda Hashim, Universiti Malaysia Sabah

Department of Electrical Electronic Engineering

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