通过图拉普拉斯去噪提升脑连接状态的J散度
Improving J-divergence of brain connectivity states by graph Laplacian denoising
AI总结：
本研究提出基于图拉普拉斯的脑功能连接去噪算法及新型J散度公式，可提升运动想象与静息状态的区分度、缩短估计时长，有望用于实时脑机接口。
摘要：
功能连接（FC）可表示为网络，常被用于深化对复杂任务神经基础的理解，例如脑机接口（BCI）中的运动想象（MI）检测。但连接估计的误差会影响检测性能。本研究针对常见连接估计的去噪问题展开研究，以提升不同连接状态的可检测性。具体而言，我们提出一种作用于网络图拉普拉斯矩阵的去噪算法，该算法利用了最新的图信号处理研究成果。此外，我们推导了不同状态下去噪拉普拉斯矩阵的Jensen散度的新型表达式。在合成数据上的数值模拟显示，该去噪方法提升了对应不同任务条件的连接模式的Jensen散度。进一步地，我们将拉普拉斯去噪技术应用于从MI-BCI实验期间记录的真实EEG数据估计得到的脑网络。借助我们提出的新型J散度表达式，我们能够量化运动想象状态与静息状态下FC网络之间的距离，还能分析每个拉普拉斯变量对两种状态间总J散度的贡献。在真实MI-BCI EEG数据上的实验结果表明，拉普拉斯去噪提升了运动想象与静息心理状态的区分度，并且缩短了连接估计所需的时间间隔。我们得出结论，该方法在实现连接状态的鲁棒检测方面颇具前景，同时也适合在实时BCI应用中部署。
英文摘要：
Functional connectivity (FC) can be represented as a network, and is frequently used to better understand the neural underpinnings of complex tasks such as motor imagery (MI) detection in brain-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 denoising algorithm that acts on the network graph Laplacian, which leverages recent graph signal processing results. Further, we derive a novel formulation of the Jensen divergence for the denoised Laplacian under different states. Numerical simulations on synthetic data show that the denoising method improves the Jensen divergence of connectivity patterns corresponding to different task conditions. Furthermore, we apply the Laplacian denoising technique to brain networks estimated from real EEG data recorded during MI-BCI experiments. Using our novel formulation of the J-divergence, we are able to quantify the distance between the FC networks in the motor imagery and resting states, as well as to understand the contribution of each Laplacian variable to the total J-divergence between two states. Experimental results on real MI-BCI EEG data demonstrate that the Laplacian denoising improves the separation of motor imagery and resting mental states, and shortens the time interval required for connectivity estimation. We conclude that the approach shows promise for the robust detection of connectivity states while being appealing for implementation in real-time BCI applications.
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