Modified Weighted Mean Filter to Improve the Baseline Reduction Approach for Emotion Recognition
Abstract
Doi: 10.28991/ESJ-2022-06-06-03
Full Text: PDF
Keywords
References
Zhang, J., Yin, Z., Chen, P., & Nichele, S. (2020). Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Information Fusion, 59, 103–126. doi:10.1016/j.inffus.2020.01.011.
Tyng, C. M., Amin, H. U., Saad, M. N. M., & Malik, A. S. (2017). The influences of emotion on learning and memory. Frontiers in Psychology, 8. doi:10.3389/fpsyg.2017.01454.
Made Agus Wirawan, I., Wardoyo, R., & Lelono, D. (2022). The challenges of emotion recognition methods based on electroencephalogram signals: A literature review. International Journal of Electrical and Computer Engineering, 12(2), 1508–1519. doi:10.11591/ijece.v12i2.pp1508-1519.
Yang, Y., Wu, Q., Fu, Y., Chen, X. (2018). Continuous Convolutional Neural Network with 3D Input for EEG-Based Emotion Recognition. Neural Information Processing. ICONIP 2018, Lecture Notes in Computer Science, 11307. Springer, Cham, Switzerland. doi:10.1007/978-3-030-04239-4_39.
Gasper, K., Spencer, L. A., & Hu, D. (2019). Does Neutral Affect Exist? How Challenging Three Beliefs About Neutral Affect Can Advance Affective Research. Frontiers in Psychology, 10. doi:10.3389/fpsyg.2019.02476.
Narayana, S., Prasad, R. R. V., & Warmerdam, K. (2019). Mind your thoughts: BCI using single EEG electrode. IET Cyber-Physical Systems: Theory and Applications, 4(2), 164–172. doi:10.1049/iet-cps.2018.5059.
Zhuang, N., Zeng, Y., Yang, K., Zhang, C., Tong, L., & Yan, B. (2018). Investigating patterns for self-induced emotion recognition from EEG signals. Sensors (Switzerland), 18(3), 1–22. doi:10.3390/s18030841.
Yang, Y., Wu, Q., Qiu, M., Wang, Y., & Chen, X. (2018). Emotion Recognition from Multi-Channel EEG through Parallel Convolutional Recurrent Neural Network. 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil. doi:10.1109/ijcnn.2018.8489331.
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), 453–464. doi:10.1109/JBHI.2020.2995767.
Liu, Y., Ding, Y., Li, C., Cheng, J., Song, R., Wan, F., & Chen, X. (2020). Multi-channel EEG-based emotion recognition via a multi-level features guided capsule network. Computers in Biology and Medicine, 123, 103927. doi:10.1016/j.compbiomed.2020.103927.
Zhao, Y., Yang, J., Lin, J., Yu, D., & Cao, X. (2020). A 3D Convolutional Neural Network for Emotion Recognition based on EEG Signals. 2020 International Joint Conference on Neural Networks (IJCNN). doi:10.1109/ijcnn48605.2020.9207420.
Agus Wirawan, I. M., Wardoyo, R., Lelono, D., Kusrohmaniah, S., & Asrori, S. (2021). Comparison of Baseline Reduction Methods for Emotion Recognition Based on Electroencephalogram Signals. 2021 6th International Conference on Informatics and Computing (ICIC 2021). doi:10.1109/ICIC54025.2021.9632948.
Jiang, X., Bian, G. Bin, & Tian, Z. (2019). Removal of artifacts from EEG signals: A review. Sensors (Switzerland), 19(5), 1–18,. doi:10.3390/s19050987.
Usakli, A. B. (2010). Improvement of EEG signal acquisition: An electrical aspect for state of the Art of front end. Computational Intelligence and Neuroscience, 2010. doi:10.1155/2010/630649.
Kawala-Sterniuk, A., Podpora, M., Pelc, M., Blaszczyszyn, M., Gorzelanczyk, E. J., Martinek, R., & Ozana, S. (2020). Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes. Sensors, 20(3), 807. doi:10.3390/s20030807.
Katsigiannis, S., & Ramzan, N. (2018). DREAMER: A Database for Emotion Recognition through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices. IEEE Journal of Biomedical and Health Informatics, 22(1), 98–107. doi:10.1109/JBHI.2017.2688239.
Athavipach, C., Pan-Ngum, S., & Israsena, P. (2019). A wearable in-ear EEG device for emotion monitoring. Sensors (Switzerland), 19(18), 1–16,. doi:10.3390/s19184014.
Jin, Y.-M., Luo, Y.-D., Zheng, W.-L., & Lu, B.-L. (2017). EEG-based emotion recognition using domain adaptation network. 2017 International Conference on Orange Technologies (ICOT). doi:10.1109/icot.2017.8336126.
Al-Shargie, F., Tariq, U., Alex, M., Mir, H., & Al-Nashash, H. (2019). Emotion Recognition Based on Fusion of Local Cortical Activations and Dynamic Functional Networks Connectivity: An EEG Study. IEEE Access, 7, 143550–143562. doi:10.1109/access.2019.2944008.
Song, T., Zheng, W., Lu, C., Zong, Y., Zhang, X., & Cui, Z. (2019). MPED: A Multi-Modal Physiological Emotion Database for Discrete Emotion Recognition. IEEE Access, 7, 12177–12191. doi:10.1109/access.2019.2891579.
Thammasan, N., Moriyama, K., Fukui, K., & Numao, M. (2016). Continuous Music-Emotion Recognition Based on Electroencephalogram. IEICE Transactions on Information and Systems, E99. D(4), 1234–1241. doi:10.1587/transinf.2015edp7251.
Kowalski, P. & Smyk, R., 2018. Review and comparison of smoothing algorithms for one-dimensional data noise reduction. 2018 International Interdisciplinary PhD Workshop (IIPhDW). doi:10.1109/iiphdw.2018.8388373.
Zhong, X., Yin, Z., & Zhang, J. (2020). Cross-Subject emotion recognition from EEG using Convolutional Neural Networks. 2020 39th Chinese Control Conference (CCC). doi:10.23919/ccc50068.2020.9189559.
Veeramallu, G. K. P., Anupalli, Y., Jilumudi, S. kumar, & Bhattacharyya, A. (2019). EEG based automatic emotion recognition using EMD and Random forest classifier. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). doi:10.1109/icccnt45670.2019.8944903.
Kawintiranon, K., Buatong, Y., & Vateekul, P. (2016). Online music emotion prediction on multiple sessions of EEG data using SVM. 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE). doi:10.1109/JCSSE.2016.7748921.
Koelstra, S., Mühl, C., Soleymani, M., Lee, J. S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012). DEAP: A database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing, 3(1), 18–31. doi:10.1109/T-AFFC.2011.15.
Miranda-Correa, J. A., Abadi, M. K., Sebe, N., & Patras, I. (2021). AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups. IEEE Transactions on Affective Computing, 12(2), 479–493. doi:10.1109/TAFFC.2018.2884461.
Pane, E. S., Wibawa, A. D., & Pumomo, M. H. (2018). Channel Selection of EEG Emotion Recognition using Stepwise Discriminant Analysis. 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM). doi:10.1109/CENIM.2018.8711196.
Chen, D. W., Miao, R., Yang, W. Q., Liang, Y., Chen, H. H., Huang, L., ... & Han, N. (2019). A feature extraction method based on differential entropy and linear discriminant analysis for emotion recognition. Sensors, 19(7). doi:10.3390/s19071631.
Jiang, H., Jia, J. (2020). Research on EEG Emotional Recognition Based on LSTM. Bio-inspired Computing: Theories and Applications. BIC-TA 2019, Communications in Computer and Information Science, 1160. Springer, Singapore. doi:10.1007/978-981-15-3415-7_34.
Lelono, D., Nuradi, H., Satriyo, M. R., Widodo, T. W., Dharmawan, A., & Istiyanto, J. E. (2019). Comparison of Difference, Relative and Fractional Methods for Classification of the Black Tea Based on Electronic Nose. 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM). doi:10.1109/cenim48368.2019.8973308.
Xu, T., Zhou, Y., Wang, Z., & Peng, Y. (2018). Learning Emotions EEG-based Recognition and Brain Activity: A Survey Study on BCI for Intelligent Tutoring System. Procedia Computer Science, 130, 376–382. doi:10.1016/j.procs.2018.04.056.
Al-Odienat, A. I., & Al-Mbaideen, A. A. (2015). Optimal length determination of the moving average filter for power system applications. International Journal of Innovative Computing, Information and Control, 11(2), 691–705.
Alarcão, S. M., & Fonseca, M. J. (2019). Emotions recognition using EEG signals: A survey. IEEE Transactions on Affective Computing, 10(3), 374–393. doi:10.1109/TAFFC.2017.2714671.
Bhandari, N. K., & Jain, M. (2020). Emotion recognition and classification using EEG: A review. International Journal of Scientific and Technology Research, 9(2), 1827–1836.
Aytekin, A. (2021). Comparative analysis of normalization techniques in the context of MCDM problems. Decision Making: Applications in Management and Engineering, 4(2), 1–25. doi:10.31181/dmame210402001a.
Divayana, D. G. H., Ariawan, I. P. W., Ardana, I. M., & Wayan Arta Suyasa, P. (2021). Utilization of alkin-wp-based digital library evaluation software as evaluation tool of digital library effectiveness. Emerging Science Journal, 5(5), 731–746. doi:10.28991/esj-2021-01308.
Wardoyo, R., Wirawan, I. M. A., & Pradipta, I. G. A. (2022). Oversampling Approach Using Radius-SMOTE for Imbalance Electroencephalography Datasets. Emerging Science Journal, 6(2), 382–398. doi:10.28991/ESJ-2022-06-02-013.
Liu, N., Fang, Y., Li, L., Hou, L., Yang, F., & Guo, Y. (2018). Multiple feature fusion for automatic emotion recognition using EEG signals. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/ICASSP.2018.8462518.
Zangeneh Soroush, M., Maghooli, K., Setarehdan, S. K., & Nasrabadi, A. M. (2019). A novel EEG-based approach to classify emotions through phase space dynamics. Signal, Image and Video Processing, 13(6), 1149-1156. doi:10.1007/s11760-019-01455-y.
Zheng, W. L., Zhu, J. Y., & Lu, B. L. (2019). Identifying stable patterns over time for emotion recognition from EEG. IEEE Transactions on Affective Computing, 10(3), 417–429. doi:10.1109/TAFFC.2017.2712143.
Sabour, S., Frosst, N., & Hinton, G. E. (2017). Dynamic routing between capsules. Advances in neural information processing systems 30 (NIPS 2017), 4-9 December, 2017, Long Beach, United States.
DOI: 10.28991/ESJ-2022-06-06-03
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 I Made Agus Wirawan, Retantyo Wardoyo, Danang Lelono, Sri Kusrohmaniah