EEG Emotion Recognition

Electroencephalography (EEG)-based emotion recognition plays a critical role in affective Brain-Computer Interfaces (aBCIs), yet its practical deployment remains limited by inter-subject variability, reliance on target-domain data, and unavoidable label noise. To address these challenges, we propose a Multi-domain Aggregation Transfer learning framework with domain-class prototypes (MAT) for emotion recognition under completely unseen target domains. MAT introduces a feature decoupling module to disentangle class-invariant domain features from domain-invariant class features, enabling more robust and interpretable EEG representations. A Hierarchical-Domain Aggregation (HDA) mechanism based on Maximum Mean Discrepancy (MMD) constructs superdomains to model shared distributional structures across subjects, while adaptive prototype updating refines domain and class prototypes to capture stable intrinsic representations. Moreover, a pairwise learning strategy reformulates classification as similarity estimation between sample pairs, effectively mitigating the effect of label noise. Extensive experiments on three public EEG emotion datasets (SEED, SEED-IV, and SEED-V) show that the accuracy of MAT is improved by 2.87%, 3.84%, and 2.05% compared with the state-of-the-art (SOTA) model for unseen target domains. Our results provide a promising direction for emotion recognition under real-world unseen-subject scenarios.The source code is available at https://github.com/WuCB-BCI/MAT.

Electroencephalography (EEG) classification has shown significant promise in applications ranging from neurodegenerative disease detection to brain-computer interfaces (BCIs). Whilst deep learning methods have increasingly dominated the research landscape in recent years; these models remain largely opaque 'black boxes' with limited interpretability. In this paper, we revisit the utility of feature-based classification via a systematic pipeline designed to integrate per-electrode signal statistics with cross-brain connectivity metrics. The proposed "KnowEEG" pipeline extracts 783 per-electrode statistical features alongside between-electrode connectivity statistics, utilizing a balanced sampling strategy between high-dimensional per electrode features and connectivity. This feature-based pipeline achieves performance comparable to or exceeding state-of-the-art deep learning models across five diverse classification tasks: emotion detection, mental workload classification, eyes open/closed detection, abnormal EEG classification, and event detection. This performance is achieved without the need for Graphics Processing Unit (GPU) resources. Furthermore, the pipeline provides inherent explainability through feature importance rankings. We demonstrate via the eyes open/closed task that this explainability identifies discriminative biomarkers in the occipital region that are consistent with established neuroscience literature. These results suggest that despite significant advances in deep learning, structured feature engineering remains a high performance and interpretable method for EEG classification.