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Hybrid method for optimizing emotion recognition models on electroencephalogram signals
IAES International Journal of Artificial Intelligence
ISSN 2252-8938, 2089-4872
Terbit 2025-06-01
Penerbit Institute of Advanced Engineering and Science
Bahasa eng
Abstrak
Two critical factors that need to be studied in emotion recognition are the differences in electroencephalogram (EEG) signal patterns caused by participant characteristics and EEG signals with spatial information. These factors significantly affect the resulting accuracy. The model proposed in this study can consider these factors. This model consists of the modified weighted mean filter method for the basic EEG signal smoothing process, the differential entropy method for the feature extraction process, the relative difference method for the baseline reduction, the 3D cube method for feature representation, and the continuous capsule network method for the classification process. Based on testing on three public datasets, this hybrid method can overcome factors affecting emotion recognition accuracy. This statement is based on the accuracy produced by this model, which outperformed the accuracy validated in previous studies.
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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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<dc:title xml:lang="en-US">Hybrid method for optimizing emotion recognition models on electroencephalogram signals</dc:title>
<dc:creator>Wirawan, I Made Agus</dc:creator>
<dc:creator>Ernanda Aryanto, Kadek Yota</dc:creator>
<dc:creator>Sukajaya, I N.</dc:creator>
<dc:creator>Agustini, Ni Nyoman Mestri</dc:creator>
<dc:creator>Widhiyanti Metra Putri, Dewi Arum</dc:creator>
<dc:subject xml:lang="en-US">Deep Learning;Machine Learning; Artificial Intelligence</dc:subject>
<dc:subject xml:lang="en-US">3D cube; Continuous capsule network; Differential entropy; Electroencephalogram; Emotion recognition; Modified weighted mean filter; Relative difference</dc:subject>
<dc:description xml:lang="en-US">Two critical factors that need to be studied in emotion recognition are the differences in electroencephalogram (EEG) signal patterns caused by participant characteristics and EEG signals with spatial information. These factors significantly affect the resulting accuracy. The model proposed in this study can consider these factors. This model consists of the modified weighted mean filter method for the basic EEG signal smoothing process, the differential entropy method for the feature extraction process, the relative difference method for the baseline reduction, the 3D cube method for feature representation, and the continuous capsule network method for the classification process. Based on testing on three public datasets, this hybrid method can overcome factors affecting emotion recognition accuracy. This statement is based on the accuracy produced by this model, which outperformed the accuracy validated in previous studies.</dc:description>
<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
<dc:contributor xml:lang="en-US"></dc:contributor>
<dc:date>2025-06-01</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:type xml:lang="en-US"></dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/26851</dc:identifier>
<dc:identifier>10.11591/ijai.v14.i3.pp2302-2314</dc:identifier>
<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 3: June 2025; 2302-2314</dc:source>
<dc:source>2252-8938</dc:source>
<dc:source>2089-4872</dc:source>
<dc:source>10.11591/ijai.v14.i3</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/26851/14570</dc:relation>
<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/downloadSuppFile/26851/3936</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2025 Institute of Advanced Engineering and Science</dc:rights>
<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
Sitasi dan pengindeksan
Jumlah menurut tiap indeks pada 13 Nov 2026; setiap indeks menghitung sitasi dengan cara berbeda.
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APA 7
Wirawan, I. M. A., Ernanda Aryanto, K. Y., Sukajaya, I. N., Agustini, N. N. M., & Widhiyanti Metra Putri, D. A. (2025). Hybrid method for optimizing emotion recognition models on electroencephalogram signals. IAES International Journal of Artificial Intelligence, 14(3), 2302–2314. https://doi.org/10.11591/ijai.v14.i3.pp2302-2314
Salin
IEEE
I. M. A. Wirawan, K. Y. Ernanda Aryanto, I. N. Sukajaya, N. N. M. Agustini, and D. A. Widhiyanti Metra Putri, “Hybrid method for optimizing emotion recognition models on electroencephalogram signals,” IAES International Journal of Artificial Intelligence, vol. 14, no. 3, pp. 2302–2314, 2025, doi: 10.11591/ijai.v14.i3.pp2302-2314.
Salin
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