Fast SSVEP Detection Using a Calibration-Free EEG Decoding Framework

Steady-State Visual Evoked Potential is a brain response to visual stimuli flickering at constant frequencies. It is commonly used in brain-computer interfaces for direct brain-device communication due to their simplicity, minimal training data, and high information transfer rate. Traditional methods suffer from poor performance due to reliance on prior knowledge, while deep learning achieves higher accuracy but requires substantial high-quality training data for precise signal decoding. In this paper, we propose a calibration-free EEG signal decoding framework for fast SSVEP detection. Our framework integrates Inter-Trial Remixing & Context-Aware Distribution Alignment data augmentation for EEG signals and employs a compact architecture of small fully connected layers, effectively addressing the challenge of limited EEG data availability. Additionally, we propose an Adaptive Spectrum Denoise Module that operates in the frequency domain based on global features, requiring only linear complexity to reduce noise in EEG data and improve data quality. For calibration-free classification experiments on short EEG signals from three public datasets, our framework demonstrates statistically significant accuracy advantages(p<0.05) over existing methods in the majority of cases, while requiring at least 52.7% fewer parameters and 29.9% less inference time. By eliminating the need for user-specific calibration, this advancement significantly enhances the usability of BCI systems, accelerating their commercialization and widespread adoption in real-world applications.