EEG-Based Emotion Recognition for Machine Intelligence: A Survey with an Open-Source Python Toolbox

This paper presents a survey of Electroencephalogram (EEG)-based emotion recognition, a task that utilizes the brain's electrical activity to decode genuine emotions. As a crucial research area in machine emotional intelligence, EEG-based emotion recognition (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: (1) 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; and (2) while large-capacity Transformer models usually perform strongly in subject-dependent scenarios, their performance may degrade substantially in subject-independent settings, where 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.