SSVEP Data Augmentation Based on Filter Band Masking and Random Phase Erasing

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown promising results in various applications, such as controlling prosthetic devices and augmented reality systems. However, current data-driven frequency recognition methods used to build high-performance SSVEP-BCIs often encounter overfitting and poor generalization when training data is limited. To address this issue, in this paper, we propose two potential SSVEP data augmentation methods, namely filter band masking (FBM) and random phase erasing (RPE), based on the inherent features of SSVEPs. These methods can produce high-quality supplementary training data to improve the performance of SSVEP-BCIs without parameter learning, making them easy to implement. To evaluate the proposed methods, two large-scale publicly available datasets (Benchmark and BETA) were used, and the experimental results showed that the proposed methods significantly could enhance the classification performance of baseline classifiers with a limited amount of calibration data. Specifically, evaluated on two methods, FBM increased the average accuracies by 7.40%, 8.55%, and RPE increased the average accuracies by 5.85%, 6.10%, respectively, with as few as two 1-scecond calibration trials on the Benchmark dataset. These findings demonstrate the potential of these data augmentation methods in enhancing the practicality of SSVEP-BCI for real-life scenarios.