# A Review of Deep Learning Techniques for EEG-Based Emotion Recognition: Models, Methods, and Datasets

## TL;DR

This paper reviews deep learning methods for recognizing emotions using EEG data, summarizing recent advances and suggesting future research directions.

## Contribution

The paper provides a systematic review of deep learning techniques for EEG-based emotion recognition, highlighting trends and gaps in the field.

## Key findings

- 233 articles on DL-based EEG emotion recognition were analyzed from 2020 to 2025.
- Public datasets for ER were evaluated based on stimulation procedures and emotional representation.
- The review emphasizes the need for more interpretable and data-efficient emotion recognition systems.

## Abstract

Emotion Recognition (ER) with Electroencephalography (EEG) has become a major area of focus in affective computing due to its direct measurement of the activity of the brain. ER based on EEG has also advanced with the popularity of Deep Learning (DL) and its advancements related to classification accuracy and model efficiency. This systematic review is conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and aims to provide an overview of DL-based EEG emotion recognition approaches. A comprehensive literature search was conducted across five major databases covering the publications from 2020 to 2025. The studies with EEG signals for ER using DL architectures were included in the present review. Finally, a total of 233 articles were considered after eligibility screening. To enhance the diversity of investigation, we assessed the public datasets utilized for ER based on EEG in terms of their stimulation procedures and emotional representation. Further, the provided analysis attempts to direct future research toward EEG-based emotion identification systems that are more interpretable, generalizable, and data-efficient. This systematic review aims to provide a roadmap for developing EEG-driven ER, guiding researchers toward more reliable, scalable, and practically useful systems.