Barbati Giulia, Porcaro Camillo, Zappasodi Filippo, Rossini Paolo Maria, Tecchio Franca
Department of Neuroscience, AFaR - Center of Medical Statistics and Information Technology, Fatebenefratelli Hospital, Isola Tiberina, Lungotevere degli Anguillara 12, 00153 Rome, Italy.
Clin Neurophysiol. 2004 May;115(5):1220-32. doi: 10.1016/j.clinph.2003.12.015.
To propose a noise reduction procedure for magnetoencephalography (MEG) signals introducing an automatic detection system of artifactual components (ICs) separated by an independent component analysis (ICA) algorithm, and a control cycle on reconstructed cleaned data to recovery part of non-artifactual signals possibly lost by the blind mechanism.
The procedure consisted of three main steps: (1) ICA for blind source separation (BSS); (2) automatic detection method of artifactual components, based on statistical and spectral ICs characteristics; (3) control cycle on 'discrepancy,' i.e. on the difference between original data and those reconstructed using only ICs automatically retained. Simulated data were generated as representative mixtures of some common brain frequencies, a source of internal Gaussian noise, power line interference, and two real artifacts (electrocardiogram=ECG, electrooculogram=EOG), with the adjunction of a matrix of Gaussian external noise. Three real data samples were chosen as representative of spontaneous noisy MEG data.
In simulated data the proposed set of markers selected three components corresponding to ECG, EOG and the Gaussian internal noise; in real-data examples, the automatic detection system showed a satisfactory performance in detecting artifactual ICs. 'Discrepancy' control cycle was redundant in simulated data, as expected, but it was a significant amelioration in two of the three real-data cases.
The proposed automatic detection approach represents a suitable strengthening and simplification of pre-processing data analyses. The proposed 'discrepancy' evaluation, after automatic pruning, seems to be a suitable way to render negligible the risk of loose non-artifactual activity when applying BSS methods to real data.
The present noise reduction procedure, including ICA separation phase, automatic artifactual ICs selection and 'discrepancy' control cycle, showed good performances both on simulated and real MEG data. Moreover, application to real signals suggests the procedure to be able to separate different cerebral activity sources, even if characterized by very similar frequency contents.
提出一种用于脑磁图（MEG）信号的降噪程序，该程序引入了一个由独立成分分析（ICA）算法分离的人工成分（IC）自动检测系统，以及对重建的清理后数据的控制循环，以恢复可能因盲机制而丢失的部分非人工信号。
该程序包括三个主要步骤：（1）用于盲源分离（BSS）的ICA；（2）基于IC的统计和频谱特征的人工成分自动检测方法；（3）对“差异”的控制循环，即对原始数据与仅使用自动保留的IC重建的数据之间的差异进行控制循环。模拟数据是由一些常见脑电频率、内部高斯噪声源、电源线干扰以及两个真实伪迹（心电图=ECG，眼电图=EOG）的代表性混合生成的，并附加了一个高斯外部噪声矩阵。选择了三个真实数据样本作为自发噪声MEG数据的代表。
在模拟数据中，所提出的一组标记选择了对应于ECG、EOG和高斯内部噪声的三个成分；在真实数据示例中，自动检测系统在检测人工IC方面表现出令人满意的性能。如预期的那样，“差异”控制循环在模拟数据中是多余的，但在三个真实数据案例中的两个案例中是一个显著的改进。
所提出的自动检测方法是对预处理数据分析的适当强化和简化。在自动修剪后提出的“差异”评估似乎是一种合适的方法，可以在将BSS方法应用于真实数据时将丢失非人工活动的风险降至可忽略不计。
目前的降噪程序，包括ICA分离阶段、人工IC自动选择和“差异”控制循环，在模拟和真实MEG数据上均表现出良好的性能。此外，应用于真实信号表明该程序能够分离不同的脑活动源，即使其频率成分非常相似。
