Integrating metacognitive mechanisms optimizes EEG generative models via hierarchical regularization.

Obtaining sufficient electroencephalography (EEG) signals for training deep neural networks (DNNs) in brain-computer interfaces (BCIs) is challenging due to individual differences in neural activity, which require large per-participant data to map signals to actions, while factors like movement artifacts often limit data collection. Existing advances mainly leverage generative models with various regularizers to produce sufficient EEG signals. However, selecting appropriate regularizers remains challenging. Inspired by metacognition, the human cognitive process that monitors and regulates learning and decision-making, we propose a metacognitive regulation module including three regularizers that explicitly capture EEG temporal dynamics and functional resolution, thereby improving both the diversity and similarity of generated data. Through extensive theoretical and empirical validation on two datasets, we demonstrate that our module: (1) significantly improves generative models for generating highly complex, realistic EEG activity; (2) improves generalization across different generative models; and (3) endows DNN models with enhanced human-like decision-making and adaptation capabilities.