Akhtar Muhammad Tahir, James Christopher J
Signal Processing and Control Group, Institute of Sound and Vibration Research, University of Southampton, Southampton, UK.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:4027-30. doi: 10.1109/IEMBS.2009.5333725.
Detecting artifacts produced in electroencephalographic (EEG) data by muscle activity, eye blinks and electrical noise, etc., is an important problem in EEG signal processing research. These artifacts must be corrected before further analysis because it renders subsequent analysis very error-prone. One solution is to reject the data segment if artifact is present during the observation interval, however, the rejected data segment could contain important information masked by the artifact. It has already been demonstrated that independent component analysis (ICA) can be an effective and applicable method for EEG de-noising. The goal of this paper is to propose a framework, based on ICA and wavelet denoising (WD), to improve the pre-processing of EEG signals. In particular we employ the concept of spatially-constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the given EEG data, use WD to remove any brain activity from extracted artifacts, and finally project back the artifacts to be subtracted from EEG signals to get clean EEG data. The main advantage of the proposed approach is faster computation, as all ICs are not identified in the usual manner due to the square mixing assumption. Simulation results demonstrate the effectiveness of the proposed approach in removing focal artifacts that can be well separated by SCICA.
检测脑电图（EEG）数据中由肌肉活动、眨眼和电噪声等产生的伪迹是EEG信号处理研究中的一个重要问题。在进一步分析之前必须校正这些伪迹，因为这会使后续分析非常容易出错。一种解决方案是，如果在观察间隔期间存在伪迹，则拒绝该数据段，然而，被拒绝的数据段可能包含被伪迹掩盖的重要信息。已经证明，独立成分分析（ICA）可以是一种用于EEG去噪的有效且适用的方法。本文的目标是提出一个基于ICA和小波去噪（WD）的框架，以改进EEG信号的预处理。特别是，我们采用空间约束ICA（SCICA）的概念从给定的EEG数据中提取仅包含伪迹的独立成分（IC），使用WD从提取的伪迹中去除任何脑电活动，最后将伪迹投影回EEG信号中进行减法运算以获得干净的EEG数据。所提出方法的主要优点是计算速度更快，因为由于平方混合假设，并非以通常方式识别所有IC。仿真结果证明了所提出方法在去除可被SCICA很好分离的局灶性伪迹方面的有效性。
