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VMD based wavelet hybrid denoising and improved FBCCA algorithm: a new technique for wearable SSVEP recognition

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Abstract

The brain-computer interface based on steady-state visual evoked potential (SSVEP) has gained increasing attention due to its non-invasiveness, low user training requirement, and high information transfer rate. In order to enhance the performance of SSVEP detection, we propose a denoising strategy combining variational mode decomposition (VMD) with wavelet fusion, along with an improved filter bank canonical correlation analysis (FBCCA) model for wearable SSVEP recognition. This denoising model employs detrended fluctuation analysis thresholding to identify noisy segments and applies deep filtering using discrete wavelet transform (DWT) or wavelet packet transform to the wearable data with significant noise. Finally, the FBCCA is used to classify the frequency bands segmented by the filter bank. Compared to single adaptive decomposition denoising and wavelet time–frequency denoising methods, our proposed approach focuses more on individual differences and achieves deep denoising effects through refined decomposition and adaptive IMFs selection. Experimental results demonstrate that compared to VMD denoising and adaptive wavelet denoising, our method improves classification accuracy by 0.34% and 2.59% for dry electrodes, and by 4.37% and 4.5% for wet electrodes. The hybrid model combining VMD decomposition with DWT denoising and enhanced FBCCA achieves optimal classification performance, providing a new perspective for wearable SSVEP recognition research and holds high potential for widespread application.

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Data availability

The dataset used in our study is available at the following link: http://bci.med.tsinghua.edu.cn/download.html.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant Number: 62303427); the Young Teacher Foundation of Henan Province (No. 2021GGJS093), the Key Science and Technology Program of Henan Province (Nos. 242102211058 and 232102211003), the Key Science Research Project of Colleges and Universities in Henan Province of China (No. 22A520046); the Doctor Natural Science Foundation of Zhengzhou University of Light Industry (2022BSJJZK13).

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Authors and Affiliations

  1. School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, Henan, China

    Yongquan Xia, Keyun Li, Duan Li, Jiaofen Nan, Ronglei Lu, Yinghui Meng, Fubao Zhu, Ni Yao, Chuang Han, Yanting Li, Peisen Liu & Tanxin Zhu

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  1. Yongquan Xia
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  3. Duan Li
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by YX, KL and DL. The first draft of the manuscript was written by KL and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Duan Li.

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Xia, Y., Li, K., Li, D. et al. VMD based wavelet hybrid denoising and improved FBCCA algorithm: a new technique for wearable SSVEP recognition. SIViP 18, 6157–6172 (2024). https://doi.org/10.1007/s11760-024-03304-z

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