MCD-EEG: Can a Non-Stationary Spatio-Temporal EEG Benchmark Evaluate Brainprint Recognition Across Tasks, Re-caps, and Populations?

Electroencephalography (EEG), owing to its high temporal resolution, non-invasive acquisition, convenient deployment, and compatibility with wearable devices, has become a fundamental technology for brain–computer interfaces (BCIs) and task-based brainprint recognition. However, existing public EEG datasets are generally limited to a single cognitive task and a single acquisition session, lacking realistic distribution shifts across tasks, populations, and acquisition stages (e.g., EEG cap removal and reattachment, referred to as re-cap). Consequently, they are insufficient for systematically evaluating the robustness, generalization, and fairness of EEG representation learning and brainprint recognition models in real-world scenarios. To address these challenges, we introduce MCD-EEG, a large-scale EEG benchmark designed for cross-temporal robust and neurodiversity-aware representation learning. MCD-EEG contains recordings from 45 participants, including 22 prelingually deaf individuals and 23 hearing controls, collected using 60-channel high-density EEG together with behavioral measurements. A multi-stage cognitive paradigm consisting of reading, interference, and delayed recall tasks was employed, with repeated recordings acquired across sessions. Notably, the second session involved complete EEG cap removal and reattachment, explicitly introducing realistic cross-temporal session drift. Based on this dataset, we further establish standardized benchmark tasks for within-task, cross-task, and cross-re-cap brainprint recognition, providing a new testbed for developing robust and fair EEG representation learning algorithms.