EEGEmotions-27: A Large-Scale EEG Dataset Annotated with 27 Fine-Grained Emotion Labels

A fundamental debate in emotion research concerns whether emotions should be conceptualized as dimensional constructs or as discrete categories. Conventional models—often limited to basic emotions or bipolar dimensions such as valence and arousal—fail to capture the richness and subtlety of subjective affective experiences elicited by emotional stimuli. Most existing EEG-based emotion datasets are built upon these frameworks and therefore exhibit limitations in reflecting the full complexity and diversity of emotional states. To address this gap, we present EEGEmotions-27, a novel large-scale EEG dataset annotated with 27 fine-grained emotion categories, offering significantly higher resolution in affective representation compared to currently available public EEG datasets. To evaluate the utility of the dataset, we trained a deep learning-based emotion classification model, which achieved an average classification accuracy of 62.24%—a performance level substantially exceeding the 3.70% chance baseline for 27-class classification. This result establishes a new standard for the challenging task of fine-grained emotion recognition using consumer-grade EEG hardware.