Evidence map›Paper›PMID 39725763›Full record

ArticleMedical & biological engineering & computing2025

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

Rongrong Fu, Shaoxiong Niu, Xiaolei Feng, Ye Shi, Chengcheng Jia, Jing Zhao, Guilin Wen

Abstract read
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Article in Medical & biological engineering & computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Rongrong FuMeasurement Technology and Instrumentation Key Lab of Hebei Province, Department of Electrical Engineering, Yanshan University, Qinhuangdao, China.
Shaoxiong NiuMeasurement Technology and Instrumentation Key Lab of Hebei Province, Department of Electrical Engineering, Yanshan University, Qinhuangdao, China.ORCID http://orcid.org/0009-0007-1010-4144
Xiaolei FengMeasurement Technology and Instrumentation Key Lab of Hebei Province, Department of Electrical Engineering, Yanshan University, Qinhuangdao, China.
Ye ShiSchool of Electrical Engineering and the Key Laboratory of Intelligent Rehabilitation and Neromodulation of Hebei Province, Yanshan University, Qinhuangdao, China.
Chengcheng JiaDepartment of Electrical, Computer & Biomedical Engineering, Ryerson University, Toronto, Canada.
Jing ZhaoSchool of Electrical Engineering and the Key Laboratory of Intelligent Rehabilitation and Neromodulation of Hebei Province, Yanshan University, Qinhuangdao, China. fxl06151220@163.com.
Guilin WenSchool of Mechanical Engineering, Yanshan University, Qinhuangdao, China.

Funding

Natural Science Foundation of Hebei Province F2020203070Natural Science Foundation of Hebei Province F2022203092the Central Guidance on Local Science and Technology Development Fund of Hebei Province 206Z0301Gthe National Natural Science Foundation of China 62073282
6 · The paper itself

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.

Indexed as

AlgorithmsArmBrain-Computer InterfacesEvoked Potentials, VisualRoboticsAdultElectroencephalographyFemaleHumansMaleSignal Processing, Computer-AssistedYoung AdultBrain-computer interfaceMultivariate synchronization indexMultivariate variational mode decompositionRobotic armSteady-state visual evoked potential

Identifiers

PMID39725763

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.