Department of Biomedical Engineering, Hefei University of Technology, Hefei, 230009, Anhui, China.
Department of Microelectronics, Hefei University of Technology, Hefei, 230009, Anhui, China.
Comput Biol Med. 2017 Sep 1;88:1-10. doi: 10.1016/j.compbiomed.2017.06.013. Epub 2017 Jun 21.
Electroencephalography (EEG) recordings are frequently contaminated by both ocular and muscle artifacts. These are normally dealt with separately, by employing blind source separation (BSS) techniques relying on either second-order or higher-order statistics (SOS & HOS respectively). When HOS-based methods are used, it is usually in the setting of assuming artifacts are statistically independent to the EEG. When SOS-based methods are used, it is assumed that artifacts have autocorrelation characteristics distinct from the EEG. In reality, ocular and muscle artifacts do not completely follow the assumptions of strict temporal independence to the EEG nor completely unique autocorrelation characteristics, suggesting that exploiting HOS or SOS alone may be insufficient to remove these artifacts. Here we employ a novel BSS technique, independent vector analysis (IVA), to jointly employ HOS and SOS simultaneously to remove ocular and muscle artifacts. Numerical simulations and application to real EEG recordings were used to explore the utility of the IVA approach. IVA was superior in isolating both ocular and muscle artifacts, especially for raw EEG data with low signal-to-noise ratio, and also integrated usually separate SOS and HOS steps into a single unified step.
脑电图（EEG）记录经常受到眼动和肌肉伪迹的干扰。这些通常通过使用基于二阶或更高阶统计量（SOS 和 HOS 分别）的盲源分离（BSS）技术分别处理。当使用基于 HOS 的方法时，通常假设伪迹与 EEG 在统计上是独立的。当使用基于 SOS 的方法时，假设伪迹具有与 EEG 不同的自相关特征。实际上，眼动和肌肉伪迹并不完全遵循与 EEG 严格时间独立的假设，也不完全具有独特的自相关特征，这表明单独利用 HOS 或 SOS 可能不足以去除这些伪迹。在这里，我们采用一种新的 BSS 技术，独立向量分析（IVA），同时利用 HOS 和 SOS 来去除眼动和肌肉伪迹。数值模拟和对真实 EEG 记录的应用用于探索 IVA 方法的实用性。IVA 在分离眼动和肌肉伪迹方面表现出色，尤其是对于信噪比低的原始 EEG 数据，并且还将通常分开的 SOS 和 HOS 步骤集成到单个统一步骤中。
