![](/public/journals/18/"https:/ejurnal.itenas.ac.id/public/site/images/arsyaddarlis/ef5f1c354abe0d0829d9cbf65129fac4.png)
![](/public/journals/18/"https:/ejurnal.itenas.ac.id/public/site/images/arsyaddarlis/11.jpg)
1.png)
Email Artikel ini 
Sembunyikan
Tampilkan Semua
![](/public/journals/18/"https:/ejurnal.itenas.ac.id/public/site/images/arsyaddarlis/1.jpg)
![](/public/journals/18/"https:/ejurnal.itenas.ac.id/public/site/images/arsyaddarlis/12.jpg)
Identifikasi Emosi Melalui Sinyal EEG menggunakan 3D-Convolutional Neural Network
Sari
ABSTRAK
Emosi memberikan peran penting dalam interaksi manusia yang didapat melalui respon yang tepat. Respon yang tak tepat menunjukan adanya gangguan mental sehingga diperlukan identifikasi emosi. Identifikasi dapat dilakukan menggunakan aktivitas sinyal listrik di otak menggunakan Elektroensephalogram (EEG). Karena sinyal EEG pada setiap kanal merupakan urutan data maka dijadikan multi-kanal yang direpresentasikan pada matriks agar urutan-urutan data tetap terjaga. Penggunaan matriks memadukan informasi dari ketiga dimensi (kanal x frekuensi x waktu) dapat menggambarkan kompleksitas dari sinyal EEG. Sehingga dapat mengenali pola aktivitas otak pada rentang frekuensi tertentu berkembang sepanjang waktu. Untuk menangkap informasi tersebut perlu dilakukan ekstraksi fitur agar mewakili variabel-variabel emosi. Ekstraksi dilakukan pada domain frekuensi (4-45 Hz) dan waktu menggunakan Short Time Fourier Transform (STFT) kemudian idenitifikasi menggunakan 3D Convolutional Neural Network (CNN). Eksperimen menggunakan 3D CNN menghasilkan akurasi 65.45 dengan teknik koreksi bobot Adamax.
Kata kunci: emosi, sinyal EEG, multi-kanal, STFT, 3D-CNN
ABSTRACT
Emotions play an important role in human interaction through appropriate responses. Inappropriate responses indicate a mental disorder, so identification of emotions is required. Identification can be done using electrical signal activity in the brain with Electroencephalogram (EEG). Because the EEG signal in each channel is a data sequence, it is made into a multi-channel represented in a matrix so that the data sequence is maintained. Using a matrix combining information from all three dimensions (channel x frequency x time) can describe the complexity of the EEG signal. Allowing recognition of evolving brain activity patterns within specific frequency ranges over time. Extraction is done in the frequency domain (4-45 Hz) and time using Short Time Fourier Transform (STFT), then identification using a 3D Convolutional Neural Network (CNN). Experiments using 3D CNN resulted in an accuracy of 65.45 with the Adamax weight correction technique.
Keywords: emotion, EEG signal, multi-channel, STFT, 3D-CNN
Kata Kunci
Teks Lengkap:
PDFReferensi
Deshpande, M., & Rao, V. (2018). Depression detection using emotion artificial intelligence. International Conference on Intelligent Sustainable Systems, ICISS 2017, (pp. 858–862).
Gao, Q., Wang, C. han, Wang, Z., Song, X. lin, Dong, E. zeng, & Song, Y. (2020). EEG based emotion recognition using fusion feature extraction method. Multimedia Tools and Applications, 79(37–38), 27057–27074.
Haqque, R. H. D., Djamal, E. C., & Wulandari, A. (2021). Emotion Recognition of EEG Signals Using Wavelet Filter and Convolutional Neural Networks. International Conference on Advanced Informatics: Concepts, Theory, and Application, ICAICTA 2021.
Houssein, E. H., Hammad, A., & Ali, A. A. (2022). Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review. In Neural Computing and Applications (Vol. 34).
Jang, H., & Tong, F. (2021). Convolutional neural networks trained with a developmental sequence of blurry to clear images reveal core differences between face and object processing. Journal of Vision, 21(12).
Indrawan, R., Djamal, E. C., & Ilyas, R. (2017). Identifikasi Neuropsikologis Terhadap Video Iklan Secara Real-Time Menggunakan Fast Fourier Transform dan Support Vector Machine. Seminar Nasional Aplikasi Teknologi Informasi (SNATI), (pp. 6–10).
Kumar, D. (2022). Emotion Recognition from DEAP Dataset Using SVM Classifier. International Journal of Scientific Research in Engineering and Management, 6(6), 1–18.
Lan, Z., Sourina, O., Wang, L., Scherer, R., & Muller-Putz, G. R. (2019). Domain Adaptation Techniques for EEG-Based Emotion Recognition: A Comparative Study on Two Public Datasets. IEEE Transactions on Cognitive and Developmental Systems, 11(1), 85–94.
Negi, T., & . S. (2021). Analysis and Processing of EEG Signal: A Review. SSRN Electronic Journal, (Icsmdi).
Nematollahi, J., & Firoozabadi, M. (2018). Recognition of Positive, Negative and Neutral Emotions Using Brain Connectivity Patterns. 2017 24th Iranian Conference on Biomedical Engineering and 2017 2nd International Iranian Conference on Biomedical Engineering, ICBME 2017, (December), (pp. 330–333).
Salama, E. S., El-Khoribi, R. A., Shoman, M. E., & Wahby Shalaby, M. A. (2018). EEG-based emotion recognition using 3D convolutional neural networks. International Journal of Advanced Computer Science and Applications, 9(8), 329–337.
Wang, Z. M., Hu, S. Y., & Song, H. (2019). Channel Selection Method for EEG Emotion Recognition Using Normalized Mutual Information. IEEE Access, 7, 143303–143311.
Yosi, A. N. N. M., Sidek, K. A., Yaacob, H. S., Othman, M., & Jusoh, A. Z. (2019). Emotion recognition using electroencephalogram signal. Indonesian Journal of Electrical Engineering and Computer Science, 15(2), 786–793.
Yudhana, A., Muslim, A., Wati, D. E., Puspitasari, I., Azhari, A., & Mardhia, M. M. (2020). Human emotion recognition based on EEG signal using fast fourier transform and KNearest neighbor. Advances in Science, Technology and Engineering Systems, 5(6), 1082–1088.
Zamani, F., & Wulansari, R. (2021). Emotion Classification using 1D-CNN and RNN based On DEAP Dataset. 363–378.
Zhang, R., Zeng, Y., Tong, L., Shu, J., Lu, R., Li, Z., … Yan, B. (2022). EEG Identity Authentication in Multi-Domain Features: A Multi-Scale 3D-CNN Approach. Frontiers in Neurorobotics, 16(June), 1–16.
Zheng, W. L., & Lu, B. L. (2015). Investigating Critical Frequency Bands and Channels for EEGBased Emotion Recognition with Deep Neural Networks. IEEE Transactions on Autonomous Mental Development, 7(3), 162–175.
Zheng, X., Yu, X., Yin, Y., Li, T., & Yan, X. (2021). Three-dimensional feature maps and convolutional neural network-based emotion recognition. International Journal of Intelligent Systems, 36(11), 6312–6336.
DOI: https://doi.org/10.26760/elkomika.v12i2.417
Refbacks
- Saat ini tidak ada refbacks.
_______________________________________________________________________________________________________________________
ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638
Publisher:
Department of Electrical Engineering Institut Teknologi Nasional Bandung
Address: 20th Building Institut Teknologi Nasional Bandung PHH. Mustofa Street No. 23 Bandung 40124
Contact: +627272215 (ext. 206)
Email: jte.itenas@itenas.ac.id
________________________________________________________________________________________________________________________
Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.
