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Due to the continuing development of non-invasive and portable sensor technologies, such as brain-computer interfaces (BCI), intellectuals from several fields have been interested in emotion recognition techniques. Human emotions can be recognised using a variety of behavioural cues, including gestures and body language, voice, and physiological markers. The first three, however, might be ineffective because people sometimes conceal their genuine emotions either intentionally or unknowingly. More precise and objective emotion recognition can be accomplished using physiological signals. Among other physiological signals, Electroencephalogram (EEG) is more responsive and sensitive to variation in affective states. Various EEG-based emotion recognition methods have recently been introduced. This study reviews EEG-based BCIs for emotion identification and gives an outline of the progress made in this field. A summary of the datasets and techniques utilised to evoke human emotions and various emotion models\u00a0is also given. We discuss several EEG feature extractions, feature selection\/reduction, machine learning, and deep learning algorithms in accordance with standard emotional identification process. We provide an overview of the human brain's EEG rhythms, which are closely related to emotional states. We also go over a number of EEG-based emotion identification research and compare numerous machine learning and deep learning techniques. In conclusion, this study highlights the\u00a0applications,\u00a0challenges and potential areas for future research in identification and classification of human emotional states.<\/jats:p>","DOI":"10.1007\/s10462-023-10690-2","type":"journal-article","created":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T12:02:19Z","timestamp":1707825739000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":151,"title":["Role of machine learning and deep learning techniques in EEG-based BCI emotion recognition system: a review"],"prefix":"10.1007","volume":"57","author":[{"given":"Priyadarsini","family":"Samal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Farukh","family":"Hashmi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,13]]},"reference":[{"key":"10690_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-021-06578-4","author":"AT Aadam","year":"2022","unstructured":"Aadam AT, Al-Obeidat F, Halim Z, Waqas M, Qayum F (2022) EmoPercept: EEG-based emotion classification through perceiver. 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