Title: A feature transfer-based deep neural network for wearable SSVEP-EEG signal classification

Authors: Yongquan Xia; Ronglei Lu; Chunlai Yu; Duan Li; Jiaofen Nan; Keyun Li; Zhuo Zhang

Addresses: School of Electronics and Information, Zhengzhou University of Light Industry, Henan, China ' School of Computer Science and Engineering, Zhengzhou University of Light Industry, Henan, China ' School of Information Engineering, Huanghe University of Science and Technology, Henan, China ' School of Electronics and Information, Zhengzhou University of Light Industry, Henan, China ' School of Computer Science and Engineering, Zhengzhou University of Light Industry, Henan, China ' School of Computer Science and Engineering, Zhengzhou University of Light Industry, Henan, China ' School of Electronics and Information, Zhengzhou University of Light Industry, Henan, China

Abstract: 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.

Keywords: brain-computer interface; steady-state visual evoked potential; wearable devices; feature transfer; long short-term memory; LSTM.

DOI: 10.1504/IJBET.2026.151945

International Journal of Biomedical Engineering and Technology, 2026 Vol.50 No.2, pp.87 - 108

Received: 21 Jul 2025
Accepted: 11 Sep 2025
Published online: 27 Feb 2026 *

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