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Biomimetics
Volume 10
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10.3390/biomimetics10080554
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Article

Enhanced SSVEP Bionic Spelling via xLSTM-Based Deep Learning with Spatial Attention and Filter Bank Techniques

by
Liuyuan Dong
1,
Chengzhi Xu
1,*,
Ruizhen Xie
1,
Xuyang Wang
1,
Wanli Yang
2 and
Yimeng Li
3
1
Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network, School of Computer, Hubei University of Technology, Wuhan 430068, China
2
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
3
School of Computer, Central China Normal University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Biomimetics 2025, 10(8), 554; https://doi.org/10.3390/biomimetics10080554
Submission received: 16 July 2025 / Revised: 15 August 2025 / Accepted: 15 August 2025 / Published: 21 August 2025
(This article belongs to the Special Issue Exploration of Bioinspired Computer Vision and Pattern Recognition)
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Abstract

Steady-State Visual Evoked Potentials (SSVEPs) have emerged as an efficient means of interaction in brain–computer interfaces (BCIs), achieving bioinspired efficient language output for individuals with aphasia. Addressing the underutilization of frequency information of SSVEPs and redundant computation by existing transformer-based deep learning methods, this paper analyzes signals from both the time and frequency domains, proposing a stacked encoder–decoder (SED) network architecture based on an xLSTM model and spatial attention mechanism, termed SED-xLSTM, which firstly applies xLSTM to the SSVEP speller field. This model takes the low-channel spectrogram as input and employs the filter bank technique to make full use of harmonic information. By leveraging a gating mechanism, SED-xLSTM effectively extracts and fuses high-dimensional spatial-channel semantic features from SSVEP signals. Experimental results on three public datasets demonstrate the superior performance of SED-xLSTM in terms of classification accuracy and information transfer rate, particularly outperforming existing methods under cross-validation across various temporal scales.
Keywords: steady-state visual evoked potentials; brain–computer interface; xLSTM; attention mechanism; filter bank; multi-scale feature steady-state visual evoked potentials; brain–computer interface; xLSTM; attention mechanism; filter bank; multi-scale feature

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MDPI and ACS Style

Dong, L.; Xu, C.; Xie, R.; Wang, X.; Yang, W.; Li, Y. Enhanced SSVEP Bionic Spelling via xLSTM-Based Deep Learning with Spatial Attention and Filter Bank Techniques. Biomimetics 2025, 10, 554. https://doi.org/10.3390/biomimetics10080554

AMA Style

Dong L, Xu C, Xie R, Wang X, Yang W, Li Y. Enhanced SSVEP Bionic Spelling via xLSTM-Based Deep Learning with Spatial Attention and Filter Bank Techniques. Biomimetics. 2025; 10(8):554. https://doi.org/10.3390/biomimetics10080554

Chicago/Turabian Style

Dong, Liuyuan, Chengzhi Xu, Ruizhen Xie, Xuyang Wang, Wanli Yang, and Yimeng Li. 2025. "Enhanced SSVEP Bionic Spelling via xLSTM-Based Deep Learning with Spatial Attention and Filter Bank Techniques" Biomimetics 10, no. 8: 554. https://doi.org/10.3390/biomimetics10080554

APA Style

Dong, L., Xu, C., Xie, R., Wang, X., Yang, W., & Li, Y. (2025). Enhanced SSVEP Bionic Spelling via xLSTM-Based Deep Learning with Spatial Attention and Filter Bank Techniques. Biomimetics, 10(8), 554. https://doi.org/10.3390/biomimetics10080554

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