Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control.

Journal: Medical & biological engineering & computing
PMID: 39725763

Abstract

This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance the performance both in quality and reliability. The proposed MVMD-MSI algorithm combines the advantages of multivariate variational mode decomposition (MVMD) and multivariate synchronization index (MSI). Compared to widely used algorithms, the novelty of this method is its capability of decomposing nonlinear and non-stationary EEG signals into intrinsic mode functions (IMF) across different frequency bands with the best center frequency and bandwidth. Therefore, SSVEP decoding performance can be improved by this method, and the effectiveness of MVMD-MSI is evaluated by the robot with 6 degrees-of-freedom. Offline experiments were conducted to optimize the algorithm's parameters, resulting in significant improvements. Additionally, the algorithm showed good performance even with fewer channels and shorter data lengths. In online experiments, the algorithm achieved an average accuracy of 98.31% at 1.8 s, confirming its feasibility and effectiveness for real-time SSVEP BCI-based robotic arm applications. The MVMD-MSI algorithm, as proposed, represents a significant advancement in SSVEP analysis for robotic control systems. It enhances decoding performance and shows promise for practical application in this field.

Authors

  • Rongrong Fu
    Measurement Technology and Instrumentation Key Laboratory of Hebei Province, Department of Electrical Engineering, Yanshan University, Qinhuangdao 066000, China. Electronic address: frr1102@aliyun.com.
  • Shaoxiong Niu
    Measurement Technology and Instrumentation Key Lab of Hebei Province, Department of Electrical Engineering, Yanshan University, Qinhuangdao, China.
  • Xiaolei Feng
    Department of Occupational Health and Occupational Medicine, College of Public Health, Zhengzhou University, Zhengzhou, China.
  • Ye Shi
    School of Electrical Engineering and the Key Laboratory of Intelligent Rehabilitation and Neromodulation of Hebei Province, Yanshan University, Qinhuangdao, China.
  • Chengcheng Jia
    Department of Electrical, Computer & Biomedical Engineering, Ryerson University, Toronto, Canada.
  • Jing Zhao
    Department of Pharmacy, Pharmacoepidemiology and Drug Safety Research Group, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
  • Guilin Wen
    School of Mechanical Engineering, Yanshan University, Qinhuangdao, China.

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