Hanna Jeff, Kim Cora, Müller-Voggel Nadia
University Clinic - Erlangen, Neurosurgery, Erlangen, Germany.
University Clinic - Erlangen, Neurosurgery, Erlangen, Germany.
J Neurosci Methods. 2020 Apr 1;335:108592. doi: 10.1016/j.jneumeth.2020.108592. Epub 2020 Feb 1.
Many magnetoencephalographs (MEG) contain, in addition to data channels, a set of reference channels positioned relatively far from the head that provide information on magnetic fields not originating from the brain. This information is used to subtract sources of non-neural origin, with either geometrical or least mean squares (LMS) methods. LMS methods in particular tend to be biased toward more constant noise sources and are often unable to remove intermittent noise.
To better identify and eliminate external magnetic noise, we propose performing ICA directly on the MEG reference channels. This in most cases produces several components which are clear summaries of external noise sources with distinct spatio-temporal patterns. We present two algorithms for identifying and removing such noise components from the data which can in many cases significantly improve data quality.
We performed simulations using forward models that contained both brain sources and external noise sources. First, traditional LMS-based methods were applied. While this removed a large amount of noise, a significant portion still remained. In many cases, this portion could be removed using the proposed technique, with little to no false positives.
COMPARISON WITH EXISTING METHOD(S): The proposed method removes significant amounts of noise to which existing LMS-based methods tend to be insensitive.
The proposed method complements and extends traditional reference based noise correction with little extra computational cost and low chances of false positives. Any MEG system with reference channels could profit from its use, particularly in labs with intermittent noise sources.
许多脑磁图仪（MEG）除了数据通道外，还包含一组放置在离头部相对较远位置的参考通道，这些通道提供并非源自大脑的磁场信息。该信息用于通过几何或最小均方（LMS）方法减去非神经起源的信号源。特别是LMS方法往往偏向于更恒定的噪声源，并且通常无法去除间歇性噪声。
为了更好地识别和消除外部磁噪声，我们建议直接对MEG参考通道执行独立成分分析（ICA）。在大多数情况下，这会产生几个成分，它们是具有明显时空模式的外部噪声源的清晰汇总。我们提出了两种算法，用于从数据中识别和去除此类噪声成分，这在许多情况下可以显著提高数据质量。
我们使用包含脑信号源和外部噪声源的正向模型进行了模拟。首先，应用传统的基于LMS的方法。虽然这去除了大量噪声，但仍有相当一部分残留。在许多情况下，可以使用所提出的技术去除这部分噪声，且几乎没有误报。
所提出的方法能够去除大量现有基于LMS的方法往往不敏感的噪声。
所提出的方法以很少的额外计算成本和低误报率补充并扩展了传统的基于参考的噪声校正方法。任何具有参考通道的MEG系统都可以从其使用中受益，特别是在存在间歇性噪声源的实验室中。
