A Methodological Review of Advances in Deep Learning-Based Methods for SSVEP Classification

Steady state visual evoked potentials (SSVEPs) play a key role in EEG-based Brain-Computer Interfaces (BCIs). They offer a high information transfer rate (ITR), require little user training, and resist noise. Recently, many studies have focused on developing SSVEP based EEG classification systems using CNNs, RNNs, Transformers, and hybrid structures. These evaluations usually center on accuracy, ITR, and generalization across different subjects. Some researchers have tried to study this area, but the review papers available usually have a limited viewpoint. They usually emphasize inputs, architectures, or classifications and overlook important performance measures such as accuracy, ITR, and generalization ability. Furthermore, many of these papers are outdated and do not account for recent advancements from 2022 to 2025. They provide general categories without a detailed analysis of methods or architecture. To address these gaps, we present a new survey that includes SSVEP-based EEG classification studies published between 2020 and 2025. This survey focuses on explainable AI (XAI) approaches. Our review looks at current methods and highlights key challenges. These challenges include data efficiency, practical use in the real world, and the development of solutions that don’t require calibration. We also discuss future research directions. We emphasize the need to improve hybrid models, domain adaptation, and interpretable AI to boost both performance and trust in SSVEP based BCIs.