A Detailed Analysis of Emotion using Deep Learning with the help of EEG Signals

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Abstract

Thanks to recent advances in sensor knowledge and information processing, computers can now detect and understand human emotions. Research into emotion identification is a promising avenue in many different areas. Feelings expressed by humans can take several forms. With so many uses for emotion classification—recommender schemes, cognitive load detection, etc.—EEG-based emotion identification has grown more important to the intelligence of Brain-Computer Interaction (BCI) systems. There has been a lot of recent excitement in research powered by Artificial Intelligence (AI) surrounding emotion categorization. The research paper included a comprehensive analysis of AI-powered automatic emotion identification using EEG data. The study compiles and reviews over a hundred articles on emotion recognition using literature research methodologies in this survey. The research sorts the articles into many groups based on the advances they include. When it comes to emotion identification using EEG signals, these papers are all about the methodologies and datasets. In addition, this review evaluates several sensors for emotion identification and contrasts their pros and cons. To choose appropriate algorithms and EEG datasets, researchers can benefit from a deeper grasp of current emotion identification systems, which the suggested survey can provide.

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Keywords

Artificial Intelligence; BrainComputer Interaction; Electroencephalogram; Human Emotion recognition; Deep Learning