Render EEG-Based Brain-Computer Interfaces Calibration-Free: Trade Space for Time in EEG Decoding

Goal: Electroencephalogram-based brain-computer interfaces (EEG BCIs) have broad applications in neurorehabilitation, clinical assessment, and assistive technologies. However, their practical deployment is severely limited by subject-specific calibration, which requires time-consuming data collection and model retraining for each user, significantly reducing usability. This reliance on calibration arises from the conventional "one-model-fits-all" strategy: "relying on a single general model to handle all data complexity like subject variability. When its limited generalization falls short, time must be spent on calibration to adapt the model." Methods: To address this limitation, we propose a trade-space-for-time strategy for calibration-free EEG decoding: "Instead of adapting one model to every user, we maintain a pool of compact models, including a general model and multiple biased models, where each biased model specializes in decoding a specific type of subject pattern. For a new input, the system automatically selects the most suitable model based on data characteristics, enabling instant adaptation without retraining." Compact deep learning models make this design feasible by allowing fast switching and low storage cost, which would be impractical with large-scale architectures. Results: Experiments on multiple public EEG datasets show that the proposed strategy achieves performance comparable to within-subject decoding: slightly higher in one dataset (0.7672 vs. 0.7601), nearly identical in another (0.7568 vs. 0.7572), and marginally lower in a third (0.8804 vs. 0.8888). Conclusions: These results demonstrate that our approach effectively eliminates calibration while preserving accuracy, providing a practical and scalable alternative for EEG BCIs. The framework also has potential applications in other neuroimaging modalities such as fMRI and fNIRS.