Data augmentation using masked principal component representation for deep learning-based SSVEP-BCIs

Data augmentation has been demonstrated to improve the classification accuracy of deep learning (DL) models in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), particularly when dealing with limited electroencephalography (EEG) data. However, current data augmentation methods often rely on signal-level manipulations, which may result in significant distortion of EEG signals. To overcome this limitation, this study proposes a component-level data augmentation method called Masked Principal Component Representation (MPCR). MPCR utilizes a principal component-based reconstruction approach, integrating a random masking strategy applied to principal component representations. Specifically, certain principal components are randomly selected and set to zero, thereby introducing random perturbations in the reconstructed samples. Furthermore, reconstructing samples via linear combinations of the remaining components effectively preserves the primary inherent structure of EEG signals. By expanding the input space covered by training samples, MPCR helps the trained model learn more robust features. To validate the effectiveness of MPCR, experiments are performed on two widely utilized public datasets. Experimental results indicate that MPCR substantially enhances classification accuracy across diverse DL models. Additionally, compared to various state-of-the-art data augmentation approaches, MPCR demonstrates both greater performance and high compatibility. This study proposes a simple yet effective component-level data augmentation method, contributing valuable insights for advancing data augmentation methods in EEG-based BCIs.