The steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) has attracted widespread research interest owing to its multi-target recognition capacity, high accuracy, and efficient information transmission. However, the recognition accuracy of wearable SSVEP-BCI systems remains limited. To address this issue, this study proposes a feature transfer-based bidirectional long short-term memory (FTBi-LSTM) classification model, which incorporates variational mode decomposition (VMD) and wavelet hybrid denoising for signal preprocessing. Within the framework of bidirectional signal processing, SSVEP signals and same-frequency reference signals are paired as input for the bidirectional sub-networks. Deep features are extracted using a feature transfer approach to achieve classification. Experimental results show that under a 0.5-second time window, the classification accuracies for dry and wet electrodes reached 44.71% and 68.23%, while under a 0.2-second time window, the information transfer rates (ITR) increased to 142.96 bits/min and 337.42 bits/min, respectively, demonstrating the effectiveness of the FTBi-LSTM model in wearable SSVEP-BCI systems.
Xia et al. (Thu,) studied this question.