{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T17:05:46Z","timestamp":1793379946942,"version":"4.5.0"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Council of Brazil","award":["305359\/2021-5"],"award-info":[{"award-number":["305359\/2021-5"]}]},{"name":"National Research Council of Brazil","award":["442182\/2023-6"],"award-info":[{"award-number":["442182\/2023-6"]}]},{"name":"National Research Council of Brazil","award":["UNI-0210-00699.01.00\/23"],"award-info":[{"award-number":["UNI-0210-00699.01.00\/23"]}]},{"name":"Internal Simplified Call PRPI\/Postgraduate\u2014Grant Support for IFCE Stricto Sensu Postgraduate Programs","award":["305359\/2021-5"],"award-info":[{"award-number":["305359\/2021-5"]}]},{"name":"Internal Simplified Call PRPI\/Postgraduate\u2014Grant Support for IFCE Stricto Sensu Postgraduate Programs","award":["442182\/2023-6"],"award-info":[{"award-number":["442182\/2023-6"]}]},{"name":"Internal Simplified Call PRPI\/Postgraduate\u2014Grant Support for IFCE Stricto Sensu Postgraduate Programs","award":["UNI-0210-00699.01.00\/23"],"award-info":[{"award-number":["UNI-0210-00699.01.00\/23"]}]},{"name":"FUNCAP","award":["305359\/2021-5"],"award-info":[{"award-number":["305359\/2021-5"]}]},{"name":"FUNCAP","award":["442182\/2023-6"],"award-info":[{"award-number":["442182\/2023-6"]}]},{"name":"FUNCAP","award":["UNI-0210-00699.01.00\/23"],"award-info":[{"award-number":["UNI-0210-00699.01.00\/23"]}]},{"name":"CAPES","award":["305359\/2021-5"],"award-info":[{"award-number":["305359\/2021-5"]}]},{"name":"CAPES","award":["442182\/2023-6"],"award-info":[{"award-number":["442182\/2023-6"]}]},{"name":"CAPES","award":["UNI-0210-00699.01.00\/23"],"award-info":[{"award-number":["UNI-0210-00699.01.00\/23"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain\u2019s electrical activity through electrodes placed on the scalp\u2019s surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain\u2013computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was \u201csubject-dependent\u201d. In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.<\/jats:p>","DOI":"10.3390\/bioengineering11080782","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T09:34:17Z","timestamp":1722591257000},"page":"782","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Emotion Detection from EEG Signals Using Machine Deep Learning Models"],"prefix":"10.3390","volume":"11","author":[{"given":"Jo\u00e3o Vitor Marques Rabelo","family":"Fernandes","sequence":"first","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Engenharia de Telecomunica\u00e7\u00f5es, Instituto Federal do Cear\u00e1 (IFCE), Fortaleza 60040-215, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6134-5366","authenticated-orcid":false,"given":"Auzuir Ripardo de","family":"Alexandria","sequence":"additional","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Engenharia de Telecomunica\u00e7\u00f5es, Instituto Federal do Cear\u00e1 (IFCE), Fortaleza 60040-215, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6472-8784","authenticated-orcid":false,"given":"Jo\u00e3o Alexandre Lobo","family":"Marques","sequence":"additional","affiliation":[{"name":"Laboratory of Applied Neurosciences (LAN), University of Saint Joseph, Saint Joseph 999078, Macau"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0750-5784","authenticated-orcid":false,"given":"D\u00e9bora Ferreira de","family":"Assis","sequence":"additional","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Engenharia de Teleinform\u00e1tica, Universidade Federal do Cear\u00e1, Fortaleza 60455-760, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1849-2018","authenticated-orcid":false,"given":"Pedro Crosara","family":"Motta","sequence":"additional","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Engenharia Biom\u00e9dica, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-598, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8189-7187","authenticated-orcid":false,"given":"Bruno Riccelli dos Santos","family":"Silva","sequence":"additional","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Engenharia de Teleinform\u00e1tica, Universidade Federal do Cear\u00e1, Fortaleza 60455-760, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,2]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"EEG Based Emotion Recognition: A Tutorial and Review","volume":"55","author":"Li","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"51001","DOI":"10.1088\/1741-2552\/ab260c","article-title":"Deep learning-based electroencephalography analysis: A systematic review","volume":"16","author":"Roy","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, X., Ren, Y., Luo, Z., He, W., Hong, J., and Huang, Y. (2023). Deep learning-based EEG emotion recognition: Current trends and future perspectives. Front. Psychol., 14.","DOI":"10.3389\/fpsyg.2023.1126994"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1109\/TAFFC.2020.2994159","article-title":"EEG-Based Emotion Recognition Using Regularized Graph Neural Networks","volume":"13","author":"Zhong","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TAFFC.2017.2714671","article-title":"Emotions Recognition Using EEG Signals: A Survey","volume":"10","author":"Alarcao","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"026012","DOI":"10.1088\/1741-2552\/ac5c8d","article-title":"Identifying similarities and differences in emotion recognition with EEG and eye movements among Chinese, German, and French People","volume":"19","author":"Liu","year":"2022","journal-title":"J. Neural Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., and Yang, X. (2018). A Review of Emotion Recognition Using Physiological Signals. Sensors, 18.","DOI":"10.3390\/s18072074"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"140990","DOI":"10.1109\/ACCESS.2019.2944001","article-title":"A Review, Current Challenges, and Future Possibilities on Emotion Recognition Using Machine Learning and Physiological Signals","volume":"7","author":"Bota","year":"2019","journal-title":"IEEE Access"},{"key":"ref_9","unstructured":"Gedeon, T., Wong, K.W., and Lee, M. (2019). A Cross-Culture Study on Multimodal Emotion Recognition Using Deep Learning. Proceedings of the Neural Information Processing, Springer. Communications in Computer and Information Science."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/TAFFC.2021.3051332","article-title":"SparseDGCNN: Recognizing Emotion From Multichannel EEG Signals","volume":"14","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1049\/cit2.12180","article-title":"Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey","volume":"8","author":"Gheisari","year":"2023","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1109\/TAFFC.2017.2712143","article-title":"Identifying Stable Patterns over Time for Emotion Recognition from EEG","volume":"10","author":"Zheng","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/TCDS.2020.2999337","article-title":"A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition","volume":"13","author":"Li","year":"2021","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106954","DOI":"10.1016\/j.asoc.2020.106954","article-title":"EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM","volume":"100","author":"Yongqiang","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1109\/TAFFC.2019.2937768","article-title":"GCB-Net: Graph Convolutional Broad Network and Its Application in Emotion Recognition","volume":"13","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jeong, D.K., Kim, H.G., and Kim, J.Y. (2023). Emotion Recognition Using Hierarchical Spatiotemporal Electroencephalogram Information from Local to Global Brain Regions. Bioengineering, 10.","DOI":"10.3390\/bioengineering10091040"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.1049\/cit2.12174","article-title":"Self-training maximum classifier discrepancy for EEG emotion recognition","volume":"8","author":"Zhang","year":"2023","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dwivedi, A.K., Verma, O.P., and Taran, S. (2024, January 21\u201322). EEG-Based Emotion Recognition Using Optimized Deep-Learning Techniques. Proceedings of the 2024 11th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India.","DOI":"10.1109\/SPIN60856.2024.10512074"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fan, C., Xie, H., Tao, J., Li, Y., Pei, G., Li, T., and Lv, Z. (2024). ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition. Biomed. Signal Process. Control, 87.","DOI":"10.1016\/j.bspc.2023.105422"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Roshdy, A., Karar, A., Kork, S.A., Beyrouthy, T., and Nait-ali, A. (2024). Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration. Appl. Sci., 14.","DOI":"10.3390\/app14062487"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.1007\/s11831-023-09920-1","article-title":"Convolutional Neural Network-Based EEG Signal Analysis: A Systematic Review","volume":"30","author":"Rajwal","year":"2023","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Abhang, P.A., Gawali, B.W., and Mehrotra, S.C. (2016). Chapter 2\u2014Technological Basics of EEG Recording and Operation of Apparatus. Introduction to EEG- and Speech-Based Emotion Recognition, Academic Press.","DOI":"10.1016\/B978-0-12-804490-2.00002-6"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"31005","DOI":"10.1088\/1741-2552\/aab2f2","article-title":"A review of classification algorithms for EEG-based brain\u2013computer interfaces: A 10 year update","volume":"15","author":"Lotte","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"DEAP: A Database for Emotion Analysis; Using Physiological Signals","volume":"3","author":"Koelstra","year":"2012","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/JBHI.2017.2688239","article-title":"DREAMER: A Database for Emotion Recognition through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices","volume":"22","author":"Katsigiannis","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_26","first-page":"311","article-title":"Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting","volume":"174","author":"Lee","year":"2022","journal-title":"Proc. Conf. Health Inference Learn."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"180308","DOI":"10.1038\/sdata.2018.308","article-title":"A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults","volume":"6","author":"Babayan","year":"2019","journal-title":"Sci. Data"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/T-AFFC.2011.25","article-title":"A Multimodal Database for Affect Recognition and Implicit Tagging","volume":"3","author":"Soleymani","year":"2012","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"012081","DOI":"10.1088\/1742-6596\/90\/1\/012081","article-title":"Artifact removal from EEG signals using adaptive filters in cascade","volume":"90","author":"Correa","year":"2007","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1109\/TAFFC.2014.2339834","article-title":"Feature Extraction and Selection for Emotion Recognition from EEG","volume":"5","author":"Jenke","year":"2014","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.cogr.2021.04.001","article-title":"Review of the emotional feature extraction and classification using EEG signals","volume":"1","author":"Wang","year":"2021","journal-title":"Cogn. Robot."},{"key":"ref_32","unstructured":"Sanei, S., and Chambers, J. (2013). EEG Signal Processing, Wiley."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1528","DOI":"10.1109\/TAFFC.2020.3013711","article-title":"An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals","volume":"13","author":"Du","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ahmed, M.Z.I., Sinha, N., Phadikar, S., and Ghaderpour, E. (2022). Automated Feature Extraction on AsMap for Emotion Classification Using EEG. Sensors, 22.","DOI":"10.3390\/s22062346"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/1741-2560\/4\/2\/R01","article-title":"A review of classification algorithms for EEG-based brain-computer interfaces","volume":"4","author":"Lotte","year":"2007","journal-title":"J. Neural Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1109\/TETC.2021.3087174","article-title":"Graph-Embedded Convolutional Neural Network for Image-Based EEG Emotion Recognition","volume":"10","author":"Song","year":"2022","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","article-title":"EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks","volume":"11","author":"Song","year":"2018","journal-title":"IEEE Trans. Affect. Comput."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/8\/782\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:28:38Z","timestamp":1760110118000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/8\/782"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,2]]},"references-count":38,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["bioengineering11080782"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering11080782","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,2]]}}}