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A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals

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Abstract

Emotion recognition using electroencephalography (EEG) is becoming an interesting topic among researchers. It has made a remarkable entry in the domain of biomedical, smart environment, brain-computer interface (BCI), communication, security, and safe driving. In the past decade, several studies have been published that viewed emotion recognition tasks in a variety of manners. Multiple algorithms have been developed to accurately capture the EEG signal and identify the emotions from such EEG signals. The advent of artificial intelligence (AI) has changed the landscape of every application including emotion recognition. Two categories of AI-based algorithms such as machine learning and deep learning algorithms are becoming popular in the emotion recognition domain. This narrative review is an attempt to provide deep insight into the AI-based techniques, their role in EEG-based emotion recognition, and their potential future possibilities in accurate emotion identification. Furthermore, this review also provides an overview of the several important topics in emotion recognition such as emotion paradigms, EEG and its processing, and the public databases.

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    Kranti Kamble & Joydeep Sengupta

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Kamble, K., Sengupta, J. A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals. Multimed Tools Appl 82, 27269–27304 (2023). https://doi.org/10.1007/s11042-023-14489-9

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