SSVEP-BiMA: Bifocal Masking Attention Leveraging Native and Symmetric-Antisymmetric Components for Robust SSVEP Decoding

TL;DR

This paper introduces SSVEP-BiMA, a novel attention-based method that combines native and symmetric-antisymmetric components to improve the accuracy and robustness of SSVEP decoding in brain-computer interfaces, especially across subjects.

Contribution

The proposed SSVEP-BiMA method leverages multiple signal representations and a bifocal masking attention mechanism to enhance cross-subject SSVEP decoding performance.

Findings

Outperforms baseline methods in accuracy and ITR on public datasets.

Enhances generalization across different subjects.

Improves robustness and prediction accuracy of SSVEP decoding.

Abstract

Brain-computer interface (BCI) based on steady-state visual evoked potentials (SSVEP) is a popular paradigm for its simplicity and high information transfer rate (ITR). Accurate and fast SSVEP decoding is crucial for reliable BCI performance. However, conventional decoding methods demand longer time windows, and deep learning models typically require subject-specific fine-tuning, leaving challenges in achieving optimal performance in cross-subject settings. This paper proposed a biofocal masking attention-based method (SSVEP-BiMA) that synergistically leverages the native and symmetric-antisymmetric components for decoding SSVEP. By utilizing multiple signal representations, the network is able to integrate features from a wider range of sample perspectives, leading to more generalized and comprehensive feature learning, which enhances both prediction accuracy and robustness. We performed experiments on two public datasets, and the results demonstrate that our proposed method surpasses baseline approaches in both accuracy and ITR. We believe that this work will contribute to the development of more efficient SSVEP-based BCI systems.