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

• URL: http://arxiv.org/abs/2410.09767v1
• Date: Sun, 13 Oct 2024 07:51:39 GMT
• Title: LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-based Emotion Recognition
• Authors: Huan Liu, Shusen Yang, Yuzhe Zhang, Mengze Wang, Fanyu Gong, Chengxi Xie, Guanjian Liu, Dalin Zhang,
• Abstract summary: We propose LibEER, a comprehensive benchmark and algorithm library for fair comparisons in EEG-based emotion recognition. LibEER establishes a unified evaluation framework with standardized experimental settings, enabling unbiased evaluations of over ten representative deep learning-based EER models.
• Score: 14.57188626072955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: EEG-based emotion recognition (EER) is garnering increasing attention due to its potential in understanding and analyzing human emotions. Recently, significant advancements have been achieved using various deep learning-based techniques to address the EER problem. However, the absence of a convincing benchmark and open-source codebase complicates fair comparisons between different models and poses reproducibility challenges for practitioners. These issues considerably impede progress in this field. In light of this, we propose a comprehensive benchmark and algorithm library (LibEER) for fair comparisons in EER by making most of the implementation details of different methods consistent and using the same single codebase in PyTorch. In response to these challenges, we propose LibEER, a comprehensive benchmark and algorithm library for fair comparisons in EER, by ensuring consistency in the implementation details of various methods and utilizing a single codebase in PyTorch. LibEER establishes a unified evaluation framework with standardized experimental settings, enabling unbiased evaluations of over ten representative deep learning-based EER models across the four most commonly used datasets. Additionally, we conduct an exhaustive and reproducible comparison of the performance and efficiency of popular models, providing valuable insights for researchers in selecting and designing EER models. We aspire for our work to not only lower the barriers for beginners entering the field of EEG-based emotion recognition but also promote the standardization of research in this domain, thereby fostering steady development. The source code is available at \url{https://github.com/ButterSen/LibEER}.

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