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Spatio-Temporal Graph Neural Network Based on Nonlinear Time–Frequency Features for Mu-ERD Classification in Multi-Session EEG Motor Imagery

Journal of Applied Engineering and Technological Science (JAETS) · Vol. 7 No. 2 (2026) · pp. 1580–1590

DOI
10.37385/jaets.v7i2.8679
ISSN
2715-6079, 2715-6087
Published
2026-06-15
Publisher
Yayasan Riset dan Pengembangan Intelektual (YRPI)
Language
en

Abstract

Mu rhythm event-related desynchronization (ERD) is a key indicator of motor imagery activity based on EEG signals. However, accurate classification of ERD remains challenging due to the nonlinear nature of EEG signals and inter-session variability. This study proposes a motor imagery classification approach using a Spatio-Temporal Graph Neural Network (ST-GNN) model that leverages nonlinear time-frequency features extracted via Variational Mode Decomposition (VMD) and Synchrosqueezing Transform (SST). The dataset was collected from a single healthy subject across five separate sessions, each consisting of two conditions: relaxation and motor imagery. After preprocessing and segmentation, features were extracted and represented as spatio-temporal graphs to be processed by the ST-GNN. The model was evaluated using metrics such as accuracy, F1-score, AUC-ROC, and the Session Stability Index (SSI). The results show that the ST-GNN achieved an accuracy of 94.2%, F1-score of 94.1%, and AUC-ROC of 96.1%, along with high prediction stability across sessions. This performance outperformed baseline models including CNN, CSP+SVM, and STFT+MLP.These findings support the hypothesis that ERD is a distributed brain network phenomenon and demonstrate that the ST-GNN approach with VMD/SST-derived features is a promising strategy for developing adaptive and accurate BCI systems.

Keywords

Collaboration network of the authors

19 authors · 52 co-authorship links · 11 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Pancasakti Makassar
  • Hasanuddin University
  • Institut Teknologi Bacharuddin Jusuf Habibie
  • Lambung Mangkurat University
  • Indonesia Open University
  • Cenderawasih University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Supriyadi La WungoAffiliation unknown3
Jafar JafarIndonesia Open University2
Norma NasirAffiliation unknown2
Firmansyah FirmansyahHasanuddin University2
Pertiwi IshakUniversitas Pancasakti Makassar2
Misbahuddin BuangAffiliation unknown2
Nasruddin NasruddinCenderawasih University2
Armin LawiHasanuddin University1
Mutia MaulidaAffiliation unknown1
Nurafni ShahnybAffiliation unknown1
Ampauleng AmpaulengAffiliation unknown1
Mar Athul Wazithah TbAffiliation unknown1
Rosary IrianyAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.yrpipku.com/index.php/jaets/oai, first harvested 9 Nov 2026, last seen 9 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Spatio-Temporal Graph Neural Network Based on Nonlinear Time–Frequency Features for Mu-ERD Classification in Multi-Session EEG Motor Imagery</dc:title>
	<dc:creator xml:lang="en">Aziz, Firman</dc:creator>
	<dc:creator xml:lang="en">Jeffry, Jeffry</dc:creator>
	<dc:creator xml:lang="en">Usman, Syahrul</dc:creator>
	<dc:creator xml:lang="en">Syam, Rahmat Fuadi</dc:creator>
	<dc:creator xml:lang="en">Arafah, Muhammad Nur</dc:creator>
	<dc:creator xml:lang="en">Mustamin, Nurul Fathanah</dc:creator>
	<dc:subject xml:lang="en">Classification</dc:subject>
	<dc:subject xml:lang="en">EEG</dc:subject>
	<dc:subject xml:lang="en">Motor Imagery</dc:subject>
	<dc:subject xml:lang="en">ERD</dc:subject>
	<dc:subject xml:lang="en">ST-GNN</dc:subject>
	<dc:description xml:lang="en">Mu rhythm event-related desynchronization (ERD) is a key indicator of motor imagery activity based on EEG signals. However, accurate classification of ERD remains challenging due to the nonlinear nature of EEG signals and inter-session variability. This study proposes a motor imagery classification approach using a Spatio-Temporal Graph Neural Network (ST-GNN) model that leverages nonlinear time-frequency features extracted via Variational Mode Decomposition (VMD) and Synchrosqueezing Transform (SST). The dataset was collected from a single healthy subject across five separate sessions, each consisting of two conditions: relaxation and motor imagery. After preprocessing and segmentation, features were extracted and represented as spatio-temporal graphs to be processed by the ST-GNN. The model was evaluated using metrics such as accuracy, F1-score, AUC-ROC, and the Session Stability Index (SSI). The results show that the ST-GNN achieved an accuracy of 94.2%, F1-score of 94.1%, and AUC-ROC of 96.1%, along with high prediction stability across sessions. This performance outperformed baseline models including CNN, CSP+SVM, and STFT+MLP.These findings support the hypothesis that ERD is a distributed brain network phenomenon and demonstrate that the ST-GNN approach with VMD/SST-derived features is a promising strategy for developing adaptive and accurate BCI systems.</dc:description>
	<dc:publisher xml:lang="en">Yayasan Riset dan Pengembangan Intelektual (YRPI)</dc:publisher>
	<dc:date>2026-06-15</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.yrpipku.com/index.php/jaets/article/view/8679</dc:identifier>
	<dc:identifier>10.37385/jaets.v7i2.8679</dc:identifier>
	<dc:source xml:lang="en">Journal of Applied Engineering and Technological Science (JAETS); Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS); 1580-1590</dc:source>
	<dc:source>2715-6079</dc:source>
	<dc:source>2715-6087</dc:source>
	<dc:source>10.37385/jaets.v7i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.yrpipku.com/index.php/jaets/article/view/8679/6427</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Firman Aziz, Jeffry Jeffry, Syahrul Usman, Rahmat Fuadi Syam, Muhammad Nur Arafah, Nurul Fathanah Mustamin</dc:rights>
</oai_dc:dc>