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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021
摘要
Functional connectivity (FC) can be represented as a 網絡, and is frequently used to better understand the 神經 underpinnings of complex tasks such as motor imagery (MI) detection in 大腦-computer interfaces (BCIs). However, errors in the estimation of connectivity can affect the detection performances. In this work, we address the problem of denoising common connectivity estimates to improve the detectability of different connectivity states. Specifically, we propose a graph signal processing based denoising algorithm that acts on the 網絡 graph Laplacian. Further, we derive a novel formulation of the Jensen divergence for the denoised Laplacian under different states. Numerical simulations on synthetic data show that denoising improves the Jensen divergence of connectivity patterns corresponding
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