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

Journal of Robotics and Control (JRC) · Vol. 6 No. 4 (2025) · hlm. 2077–2094

DOI
10.18196/jrc.v6i4.27084
ISSN
2715-5072, 2715-5056
Terbit
2025-08-10
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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  • Institut Teknologi Sepuluh Nopember
  • Institut Teknologi Dan Bisnis STIKOM Bali
  • Rajamangala University of Technology
  • University of Indonesia
  • National Taiwan University of Science and Technology
  • Universiti of Malaysia Sabah
  • Indonesian Institute of Sciences
  • Organisasi lain
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
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Rekan penulisOrganisasiArtikel bersama
Yamin ThweRajamangala University of Technology5
Yuri PamungkasInstitut Teknologi Sepuluh Nopember4
Dechrit ManeethamRajamangala University of Technology3
I Komang Agus Ady AryantoRajamangala University of Technology2
Abdul KarimHallym University2
Tegar Anugrah FirdausInstitut Teknologi Sepuluh Nopember2
Bryan RaisInstitut Teknologi Sepuluh Nopember2
Marcelinus Jonathan SalimInstitut Teknologi Sepuluh Nopember2
Andhika Rastra NegaraInstitut Teknologi Sepuluh Nopember2
Muhammad Qaiser Zaydan ZiaInstitut Teknologi Sepuluh Nopember2
Padma Nyoman CrisnapatiRajamangala University of Technology1
I Gusti Ngurah Satria WijayaInstitut Teknologi Dan Bisnis STIKOM Bali1
Riza WulandariInstitut Teknologi Dan Bisnis STIKOM Bali1
Ni Wayan Cahya Ayu PratamiInstitut Teknologi Dan Bisnis STIKOM Bali1
I Ketut Putu SuniantaraInstitut Teknologi Dan Bisnis STIKOM Bali1
Candra AhmadiNational Taiwan University of Science and Technology1
Ni Ketut Dewi Ari JayantiInstitut Teknologi Dan Bisnis STIKOM Bali1
Gde SastrawangsaInstitut Teknologi Dan Bisnis STIKOM Bali1
Ni Wayan DerianiInstitut Teknologi Dan Bisnis STIKOM Bali1
Kartina Diah Kesuma WardhaniCaltex Riau Polytechnic1
Muhammad Imam SuryaIndonesian Institute of Sciences1
Siti Fauziyah RahmanUniversity of Indonesia1
Irfan MartiansyahIndonesian Institute of Sciences1
Wiguna RahmanNational Research and Innovation Agency1
Yudan WhulanzaUniversity of Indonesia1
Adella JosephinUniversity of Indonesia1
Kenny LischerUniversity of Indonesia1
Irzaman IrzamanIPB University1
Agus SubagioDiponegoro University1
Pepen ArifinBandung Institute of Technology1
Fitri Suryani ArsyadSriwijaya University1
Budi MulyantiIndonesia University of Education1
M. BarmawiBandung Institute of Technology1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">A Comprehensive Review of EEGLAB for EEG Signal Processing: Prospects and Limitations</dc:title>
	<dc:creator>Pamungkas, Yuri</dc:creator>
	<dc:creator>Rangkuti, Rahmah Yasinta</dc:creator>
	<dc:creator>Triandini, Evi</dc:creator>
	<dc:creator>Nakkliang, Kanittha</dc:creator>
	<dc:creator>Yunanto, Wawan</dc:creator>
	<dc:creator>Uda, Muhammad Nur Afnan</dc:creator>
	<dc:creator>Hashim, Uda</dc:creator>
	<dc:subject xml:lang="en-US">EEGLAB</dc:subject>
	<dc:subject xml:lang="en-US">EEG Signal Processing</dc:subject>
	<dc:subject xml:lang="en-US">ICA</dc:subject>
	<dc:subject xml:lang="en-US">Artifact Removal</dc:subject>
	<dc:subject xml:lang="en-US">EEG Connectivity Analysis</dc:subject>
	<dc:description xml:lang="en-US">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 &amp;amp; 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.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2025-08-10</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/27084</dc:identifier>
	<dc:identifier>10.18196/jrc.v6i4.27084</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 6 No. 4 (2025); 2077-2094</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/27084/11809</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Yuri Pamungkas, Rahmah Yasinta Rangkuti, Evi Triandini, Kanittha Nakkliang, Wawan Yunanto, Muhammad Nur Afnan Uda, Uda Hashim</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>