LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-based Emotion Recognition

TL;DR

LibEER is a comprehensive benchmark and open-source library that standardizes evaluation and comparison of deep learning models for EEG-based emotion recognition, addressing reproducibility and fairness issues in the field.

Contribution

It introduces a standardized framework with baseline models, harmonized implementation, and extensive evaluation across datasets, facilitating fair comparisons and reproducibility in EER research.

Findings

Thorough comparison of 17 deep learning models for EER

Identification of current challenges in EEG-based emotion recognition

Insights into model performance and efficiency

Abstract

EEG-based emotion recognition (EER) has gained significant attention due to its potential for understanding and analyzing human emotions. While recent advancements in deep learning techniques have substantially improved EER, the field lacks a convincing benchmark and comprehensive open-source libraries. This absence complicates fair comparisons between models and creates reproducibility challenges for practitioners, which collectively hinder progress. To address these issues, we introduce LibEER, a comprehensive benchmark and algorithm library designed to facilitate fair comparisons in EER. LibEER carefully selects popular and powerful baselines, harmonizes key implementation details across methods, and provides a standardized codebase in PyTorch. By offering a consistent evaluation framework with standardized experimental settings, LibEER enables unbiased assessments of seventeen representative deep learning models for EER across the six most widely used datasets. Additionally, we conduct a thorough, reproducible comparison of model performance and efficiency, providing valuable insights to guide researchers in the selection and design of EER models. Moreover, we make observations and in-depth analysis on the experiment results and identify current challenges in this community. We hope that our work will not only lower entry barriers for newcomers to EEG-based emotion recognition but also contribute to the standardization of research in this domain, fostering steady development. The library and source code are publicly available at https://github.com/XJTU-EEG/LibEER.