Mannan Malik M Naeem, Jeong Myung Y, Kamran Muhammad A
Department of Cogno-Mechatronics Engineering, Pusan National University Busan, South Korea.
Front Hum Neurosci. 2016 May 3;10:193. doi: 10.3389/fnhum.2016.00193. eCollection 2016.
Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG.
脑电图（EEG）是一种便携式脑成像技术，具有高时间分辨率的优势，可用于记录大脑的电活动。然而，由于眼电伪迹的干扰，脑电图信号难以分析，这可能会导致得出误导性结论。此外，事实证明，眼电伪迹的干扰会降低脑机接口（BCI）的分类准确率。因此，在对脑电图信号进行分析以用于BCI等应用之前，去除/减少这些伪迹非常重要。本文提出了一种结合独立成分分析（ICA）、回归和高阶统计的混合框架，以从脑电图数据中识别并消除伪迹活动。我们使用模拟、实验和标准脑电图信号来评估和分析所提方法的有效性。结果表明，所提方法能够有效去除眼电伪迹，同时保留脑电图数据中的神经元信号。与文献中的四种方法，即ICA、回归分析、小波ICA（wICA）和回归ICA（REGICA）进行比较，结果证实了所提方法在去除脑电图眼电活动方面具有显著增强的性能和有效性，表现为较低的均方误差和平均绝对误差值，以及重建脑电图与原始脑电图之间较高的互信息。
