Boosting SSVEP Multi-subject Classification Through Mixture of ResNet-Based Experts

Accurate classification of electroencephalographic (EEG) signals in multi-subject Brain–Computer Interfaces (BCIs) is challenged by high inter-subject variability. When data from different individuals are aggregated, distribution shifts and subject-specific patterns may introduce noise and reduce the ability of machine learning models to learn robust representations. This issue is particularly critical in Steady-State Visually Evoked Potential (SSVEP)-based BCIs, where precise frequency-specific responses must be reliably detected. In this work, we propose a Mixture-of-Experts (MoE) framework for SSVEP classification. Each expert is a Multi-Level ResNet trained on one or multiple subjects, while a gating network learns to combine experts’ predictions. The framework is evaluated on a real-world multi-subject SSVEP dataset acquired using a Bitbrain Diadem IoT device. Results show improved classification performance and computational efficiency compatible with real-time deployment on devices with limited processing capabilities.