Novel Sinusoidal Signal Assisted Multivariate Variational Mode Decomposition Combined with Task-Related Component Analysis for Enhancing SSVEP-Based BCI Performance
J. Lyu, Y.-K. Yang, Y. Zong, Y. Leng, W. Zheng, S. Ge.
In the steady-state visually evoked potential (SSVEP) based brain-computer interfaces (BCIs), each harmonic component of SSVEP individually contains unique features. The effective detection of harmonic components will enhance the recognition performance of SSVEP-based BCIs.
This study proposes a sinusoidal signal assisted multivariate variational mode decomposition (SA-MVMD) algorithm that allows the constraint of the center frequencies and narrowband filtering structures of the intrinsic mode functions (IMFs) based on the prior frequency knowledge of the signal. It preserves the target information of the signal during decomposition while avoiding mode mixing and incorrect decomposition, thus enabling the effective extraction of each independent harmonic component of SSVEP. On this basis, a SA-MVMD based task-related component analysis (SA-MVMD-TRCA) method is further proposed to fully utilize the features within the overall SSVEP as well as its independent harmonics, which can significantly improve recognition performance.
This study confirms the effectiveness of SA-MVMD and the potential of this approach, which analyzes and utilizes each independent harmonic of SSVEP, providing new strategies and perspectives for performance enhancement in SSVEP-based BCIs.
Tags: Signal assisted multivariate variational mode decomposition, Intrinsic mode functions, Prior frequency knowledge
Sensor Informatics
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Imaging Informatics
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Medical Informatics
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Bioinformatics
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Modeling and AI Informatics
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Special Issue on Trustworthy Machine Learning for Health Informatics
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