Identifikasi Emosi Melalui Sinyal Elektroensephalogram Menggunakan Graph Convolutional Network

VENA MEILINDA LIONITAMA; ESMERALDA CONTESSA DJAMAL; FATAN KASYIDI

doi:10.26760/mindjournal.v9i1.42-51

Identifikasi Emosi Melalui Sinyal Elektroensephalogram Menggunakan Graph Convolutional Network

VENA MEILINDA LIONITAMA, ESMERALDA CONTESSA DJAMAL, FATAN KASYIDI

Sari

Abstrak

Emosi merupakan bentuk respon manusia terhadap sesuatu. Pengenalan emosi menggunakan komputer dapat membantu para dokter untuk mengetahui emosi yang sedang dirasakan oleh seseorang berdasarkan aktivitas otak. Aktivitas otak dapat diketahui dengan cara merekam aktivitas sinyal Electroensephalogram (EEG). Sinyal EEG memiliki karakteristik yang berubah-ubah dan non stasioner sehingga membutuhkan metode yang dapat mengintegrasikan karakteristik temporal dan spasial. Pengenalan emosi menggunakan sinyal EEG berkaitan erat dengan pola konektivitas pada belahan otak manusia, karena setiap emosi akan memiliki pola konektivitas yang berbeda dalam belahan otak. Maka dari itu mempelajari pola konektivitas dalam belahan otak akan membantu dalam pengenalan emosi. Dan untuk menangani hal itu dibutuhkan metode deep learning yang dapat mengintegrasikan karakteristik temporal dan spasial dan dapat menerima masukan berupa pola konektivitas tersebut, metode yang dapat menanganinya yaitu, Graph Convolutional Network (GCN). Penelitian ini telah membuat sistem identifikasi emosi dengan tiga kelas menggunakan GCN dan menghasilkan akurasi data uji sebesar 35,52%.

Kata kunci: Emosi; Deep Learning; Sinyal EEG; Spasial; Temporal; GCN

AbstractEmotion is a form of human response to something. Emotion recognition using computers can help doctors to see the emotions that are being felt by a person based on brain activity. Brain activity can be known by recording electroencephalogram (EEG) signal activity. EEG signals have changing and non-stationary characteristics, requiring a method to integrate temporal and spatial characteristics. Emotion recognition using EEG signals is closely related to connectivity patterns in the human brain hemispheres because each emotion will have different connectivity patterns in the brain hemispheres. Therefore, studying the connectivity patterns in the cerebral hemispheres will help in emotion recognition. Moreover, a deep learning method is needed to integrate temporal and spatial characteristics and receive input in the form of connectivity patterns, a method that can handle Graph Convolutional Network (GCN). This research has created an emotion identification system with three classes using GCN and produced an accuracy of 35.52% of testing data.

Keywords: Emotion; Deep Learning; EEG Signal; Spatial; Temporal; GCN

Teks Lengkap:

PDF

Referensi

Algarni, M., Saeed, F., Al-Hadhrami, T., Ghabban, F., & Al-Sarem, M. (2022). Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM). Sensors, 22(8). https://doi.org/10.3390/s22082976

Cheng, J., Chen, M., Li, C., Liu, Y., Song, R., Liu, A., & Chen, X. (2021). Emotion Recognition from Multi-Channel EEG via Deep Forest. IEEE Journal of Biomedical and Health Informatics, 25(2), 453â€“464. https://doi.org/10.1109/JBHI.2020.2995767

Du, G., Su, J., Zhang, L., Su, K., Wang, X., Teng, S., & Liu, P. X. (2022). A Multi-Dimensional Graph Convolution Network for EEG Emotion Recognition. IEEE Transactions on Instrumentation and Measurement, 71. https://doi.org/10.1109/TIM.2022.3204314

Gao, Y., Fu, X., Ouyang, T., & Wang, Y. (2022). EEG-GCN: Spatio-Temporal and Self-Adaptive Graph Convolutional Networks for Single and Multi-View EEG-Based Emotion Recognition. IEEE Signal Processing Letters, 29, 1574â€“1578. https://doi.org/10.1109/LSP.2022.3179946

Gonzalez, E. J. S., & McMullen, K. (2020). The Design of an Algorithmic Modal Music Platform for Eliciting and Detecting Emotion. 8th International Winter Conference on Brain-Computer Interface, BCI 2020, 58â€“60. https://doi.org/10.1109/BCI48061.2020.9061664

Haqque, R. H. D., Djamal, E. C., & Wulandari, A. (2021). Emotion Recognition of EEG Signals Using Wavelet Filter and Convolutional Neural Networks. Proceedings - 2021 8th International Conference on Advanced Informatics: Concepts, Theory, and Application, ICAICTA 2021. https://doi.org/10.1109/ICAICTA53211.2021.9640279

Kim, S., Kim, T. S., & Lee, W. H. (2022). Accelerating 3D Convolutional Neural Network with Channel Bottleneck Module for EEG-Based Emotion Recognition. Sensors, 22(18), 1â€“16. https://doi.org/10.3390/s22186813

Li, J., Qiu, S., Du, C., Wang, Y., & He, H. (2020). Domain adaptation for eeg emotion recognition based on latent representation similarity. IEEE Transactions on Cognitive and Developmental Systems, 12(2), 344â€“353. https://doi.org/10.1109/TCDS.2019.2949306

Li, Y., Zheng, W., Zong, Y., Cui, Z., Zhang, T., & Zhou, X. (2021). A Bi-Hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition. IEEE Transactions on Affective Computing, 12(2), 494â€“504. https://doi.org/10.1109/TAFFC.2018.2885474

Liang, Z., Zhou, R., Zhang, L., Li, L., Huang, G., Zhang, Z., & Ishii, S. (2021). EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with an Application to Emotion Recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29(1), 1913â€“1925. https://doi.org/10.1109/TNSRE.2021.3111689

Putri, N. A., Contessa Djamal, E., Nugraha, F., & Kasyidi, F. (2022). EEG Emotion Recognition using Parallel Hybrid Convolutional-Recurrent Neural Networks. 2022 International Conference on Data Science and Its Applications, ICoDSA 2022, 24â€“29. https://doi.org/10.1109/ICoDSA55874.2022.9862853

Song, T., Zheng, W., Song, P., & Cui, Z. (2020). EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks. IEEE Transactions on Affective Computing, 11(3), 532â€“541. https://doi.org/10.1109/TAFFC.2018.2817622

Sun, J., Wang, X., Zhao, K., Hao, S., & Wang, T. (2022). Multi-Channel EEG Emotion Recognition Based on Parallel Transformer and 3D-Convolutional Neural Network. Mathematics, 10(17). https://doi.org/10.3390/math10173131

Zhang, H. (2020). Expression-eeg-based collaborative multimodal emotion recognition using deep autoencoder. IEEE Access, 8, 164130â€“164143. https://doi.org/10.1109/ACCESS.2020.3021994

Zheng, W. L., & Lu, B. L. (2015). Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks. IEEE Transactions on Autonomous Mental Development, 7(3), 162â€“175. https://doi.org/10.1109/TAMD.2015.2431497

DOI: https://doi.org/10.26760/mindjournal.v9i1.42-51