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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) · hlm. 1580–1590

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

Abstrak

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.

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20 penulis · 54 tautan kepenulisan bersama · 28 lintas organisasi

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  • Universitas Pancasakti Makassar
  • State University of Makassar
  • Lambung Mangkurat University
  • Hasanuddin University
  • Institut Teknologi Bacharuddin Jusuf Habibie
  • Cenderawasih University
  • Sekolah Tinggi Ilmu Ekonomi Makassar Bongaya
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Supriyadi La WungoAfiliasi tidak diketahui3
Norma NasirState University of Makassar2
Firmansyah FirmansyahHasanuddin University2
Pertiwi IshakUniversitas Pancasakti Makassar2
Misbahuddin BuangAfiliasi tidak diketahui2
Nasruddin NasruddinCenderawasih University2
Armin LawiHasanuddin University1
Mutia MaulidaLambung Mangkurat University1
Jafar JafarUniversitas Pancasakti Makassar1
Nurafni ShahnybUniversitas Pancasakti Makassar1
Ampauleng AmpaulengSekolah Tinggi Ilmu Ekonomi Makassar Bongaya1
Jafar JafarState University of Makassar1
Mar Athul Wazithah TbState University of Makassar1
Rosary IrianyAfiliasi tidak diketahui1

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.yrpipku.com/index.php/jaets/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">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>