一种基于局部密度的自适应眼电伪迹去除方法

脑电信号幅值微弱且信噪比低易受到多种伪迹影响.其中,眼电伪迹幅值高、随机性强,常使脑电信号产生明显畸变,对信号的后续分析将产生极大的影响.传统伪迹去除方法难以精确定位伪迹成分,导致过多有效信息丢失.针对上述问题,该文提出一种基于数据驱动的自适应伪迹定位和去除方法.该方法将局部密度引入独立成分分析(ICA)并通过聚类分析...

An Adaptive EOG Removal Method Based on Local Density

EEG (ElectroEncephaloGram) signal is susceptible to various of artifacts due to its low amplitude and poor SNR (Signal-Noise Ratio). Among this noise, the ocular artifacts usually hold higher amplitude and strong randomness which would cause serious distortion on EEG signal, and result in great influence on the subsequent analysis. However, traditional methods fail to locate the artifacts components accurately, leading to the loss of the efficient signal components. In order to solve the above problem, this paper proposes a data-driven based automatically artifact-localization-and-removement method. In this paper, the local density is firstly introduced into ICA (Independent Component Analysis) so as to estimate the adaptive threshold with clustering strategy. This adaptive threshold would be further used to noise localization and removal. Subsequently, this paper compared the performance differences between the proposed method and the traditional methods through simulation and the real resting-state EEG experiments. The results with indexes such as PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Square Error), and MI (Mutual Information) quantitatively verify the significant superiority of the proposed method to other ICA-based ocular artifacts removal strategies through statistical analysis.