Department of Neurology, Campus Benjamin Franklin, Charité-University Medicine Berlin, Berlin, Germany.
Neuroimage. 2011 Apr 15;55(4):1528-35. doi: 10.1016/j.neuroimage.2011.01.057. Epub 2011 Jan 27.
Neuronal oscillations have been shown to underlie various cognitive, perceptual and motor functions in the brain. However, studying these oscillations is notoriously difficult with EEG/MEG recordings due to a massive overlap of activity from multiple sources and also due to the strong background noise. Here we present a novel method for the reliable and fast extraction of neuronal oscillations from multi-channel EEG/MEG/LFP recordings. The method is based on a linear decomposition of recordings: it maximizes the signal power at a peak frequency while simultaneously minimizing it at the neighboring, surrounding frequency bins. Such procedure leads to the optimization of signal-to-noise ratio and allows extraction of components with a characteristic "peaky" spectral profile, which is typical for oscillatory processes. We refer to this method as spatio-spectral decomposition (SSD). Our simulations demonstrate that the method allows extraction of oscillatory signals even with a signal-to-noise ratio as low as 1:10. The SSD also outperformed conventional approaches based on independent component analysis. Using real EEG data we also show that SSD allows extraction of neuronal oscillations (e.g., in alpha frequency range) with high signal-to-noise ratio and with the spatial patterns corresponding to central and occipito-parietal sources. Importantly, running time for SSD is only a few milliseconds, which clearly distinguishes it from other extraction techniques usually requiring minutes or even hours of computational time. Due to the high accuracy and speed, we suggest that SSD can be used as a reliable method for the extraction of neuronal oscillations from multi-channel electrophysiological recordings.
神经元振荡被证明是大脑中各种认知、感知和运动功能的基础。然而，由于来自多个源的活动大量重叠，以及强烈的背景噪声，使用 EEG/MEG 记录来研究这些振荡是非常困难的。在这里，我们提出了一种从多通道 EEG/MEG/LFP 记录中可靠、快速提取神经元振荡的新方法。该方法基于记录的线性分解：它在峰值频率处最大化信号功率，同时在相邻的、周围的频率箱处最小化信号功率。这样的过程导致信噪比的优化，并允许提取具有特征性“峰值”光谱特征的分量，这是振荡过程的典型特征。我们将这种方法称为空间-谱分解（SSD）。我们的模拟表明，即使信噪比低至 1:10，该方法也允许提取振荡信号。SSD 也优于基于独立成分分析的传统方法。使用真实的 EEG 数据，我们还表明 SSD 允许以高信噪比提取神经元振荡（例如，在 alpha 频率范围内），并且具有与中央和枕顶源相对应的空间模式。重要的是，SSD 的运行时间仅为数毫秒，这使其明显区别于其他通常需要数分钟甚至数小时计算时间的提取技术。由于准确性和速度高，我们建议 SSD 可以作为从多通道电生理记录中提取神经元振荡的可靠方法。
