LibEMER: A novel benchmark and algorithms library for EEG-based Multimodal Emotion Recognition
This work addresses the problem of inconsistent and non-reproducible research in EEG-based multimodal emotion recognition for the machine learning and neuroscience communities, though it is incremental as it focuses on benchmarking rather than novel algorithmic advances.
The authors tackled the lack of open-source implementations and standardized benchmarks in EEG-based multimodal emotion recognition by introducing LibEMER, a unified evaluation framework that provides reproducible PyTorch implementations and standardized protocols, enabling unbiased performance assessment on three public datasets across two learning tasks.
EEG-based multimodal emotion recognition(EMER) has gained significant attention and witnessed notable advancements, the inherent complexity of human neural systems has motivated substantial efforts toward multimodal approaches. However, this field currently suffers from three critical limitations: (i) the absence of open-source implementations. (ii) the lack of standardized and transparent benchmarks for fair performance analysis. (iii) in-depth discussion regarding main challenges and promising research directions is a notable scarcity. To address these challenges, we introduce LibEMER, a unified evaluation framework that provides fully reproducible PyTorch implementations of curated deep learning methods alongside standardized protocols for data preprocessing, model realization, and experimental setups. This framework enables unbiased performance assessment on three widely-used public datasets across two learning tasks. The open-source library is publicly accessible at: https://anonymous.4open.science/r/2025ULUIUBUEUMUEUR485384