EEG-based emotion recognition for machine intelligence: a survey with an open-source Python toolbox

Yong‐Jin Liu

doi:10.1093/nsr/nwag460

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EEG-based emotion recognition for machine intelligence: a survey with an open-source Python toolbox

2026-07-30

DOI：10.1093/nsr/nwag460

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Abstract

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This paper presents a survey of electroencephalogram-based emotion recognition (EEG-ER), a task that utilizes the brain’s electrical activity to decode genuine emotions. As a crucial research area in machine emotional intelligence, EEG-ER has received extensive attention. While recent resources like TorchEEG and LibEER provide valuable coding libraries, they largely treat EEG-ER as a purely data-driven task, leaving a critical gap in bridging neurophysiological theory with computational pipeline design. To address this, we introduce an open-source Python toolbox, EEGEmoLib, accompanied by a methodological survey that explicitly integrates psychological and neuroscientific priors into algorithm design. Unlike existing reviews, we provide a structurally quantified framework incorporating 8 diverse public datasets and implementing 18 categories of handcrafted features with adaptive feature-weighting modules, 11 feature selection methods, 5 representative traditional machine learning models and 15 representative deep learning models spanning convolutional, sequential, graph-based and Transformer architectures. Using this unified codebase, we conduct rigorous benchmarking under standardized subject-dependent and subject-independent protocols. Our comparative analysis suggests two concrete methodological insights. (i) Explicitly modeling neurophysiological priors, such as hemispheric asymmetry and specific frequency band importance, can improve recognition performance in certain settings, and the effect is model-dependent rather than universal. (ii) While large-capacity Transformer models usually perform strongly in subject-dependent scenarios, shallower networks or explicit domain-adaptation models can exhibit better robustness. Detailed experimental results, methodological pitfalls and outstanding issues are discussed to guide future research. The Python toolbox EEGEmoLib is publicly available at https://eegemolib.github.io.