{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T21:04:40Z","timestamp":1783458280006,"version":"3.55.0"},"reference-count":62,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T00:00:00Z","timestamp":1649808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Deputyship for Research &amp; Innovation, Ministry of Education in Saudi Arabia","award":["77 \/442"],"award-info":[{"award-number":["77 \/442"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Emotions are an essential part of daily human communication. The emotional states and dynamics of the brain can be linked by electroencephalography (EEG) signals that can be used by the Brain\u2013Computer Interface (BCI), to provide better human\u2013machine interactions. Several studies have been conducted in the field of emotion recognition. However, one of the most important issues facing the emotion recognition process, using EEG signals, is the accuracy of recognition. This paper proposes a deep learning-based approach for emotion recognition through EEG signals, which includes data selection, feature extraction, feature selection and classification phases. This research serves the medical field, as the emotion recognition model helps diagnose psychological and behavioral disorders. The research contributes to improving the performance of the emotion recognition model to obtain more accurate results, which, in turn, aids in making the correct medical decisions. A standard pre-processed Database of Emotion Analysis using Physiological signaling (DEAP) was used in this work. The statistical features, wavelet features, and Hurst exponent were extracted from the dataset. The feature selection task was implemented through the Binary Gray Wolf Optimizer. At the classification stage, the stacked bi-directional Long Short-Term Memory (Bi-LSTM) Model was used to recognize human emotions. In this paper, emotions are classified into three main classes: arousal, valence and liking. The proposed approach achieved high accuracy compared to the methods used in past studies, with an average accuracy of 99.45%, 96.87% and 99.68% of valence, arousal, and liking, respectively, which is considered a high performance for the emotion recognition model.<\/jats:p>","DOI":"10.3390\/s22082976","type":"journal-article","created":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T23:07:16Z","timestamp":1649891236000},"page":"2976","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":132,"title":["Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM)"],"prefix":"10.3390","volume":"22","author":[{"given":"Mona","family":"Algarni","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia"},{"name":"Computer Science and Artificial Intelligence Department, College of Computer and Cyber Sciences, University of Prince Mugrin, Medina 42241, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2822-1708","authenticated-orcid":false,"given":"Faisal","family":"Saeed","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia"},{"name":"DAAI Research Group, Department of Computing and Data Science, School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7441-604X","authenticated-orcid":false,"given":"Tawfik","family":"Al-Hadhrami","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fahad","family":"Ghabban","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7172-8224","authenticated-orcid":false,"given":"Mohammed","family":"Al-Sarem","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"031001","DOI":"10.1088\/1741-2552\/ab0ab5","article-title":"Deep learning for electroencephalogram (EEG) classification tasks: A review","volume":"16","author":"Craik","year":"2019","journal-title":"J. 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