Background

Electrophysiological recordings contain mixtures of signals from distinct neural sources, impeding a straightforward interpretation of the sensor-level data. This mixing is particularly detrimental when distinct sources resonate in overlapping frequencies. Fortunately, the mixing is linear and instantaneous. Multivariate source separation methods may therefore successfully separate statistical sources, even with overlapping spatial distributions.

New Method

We demonstrate a feature-guided multivariate source separation method that is tuned to narrowband frequency content as well as binary condition differences. This method — comparison scanning generalized eigendecomposition, csGED — harnesses the covariance structure of multichannel data to find directions (i.e., eigenvectors) that maximally separate two subsets of data. To drive condition specificity and frequency specificity, our data subsets were taken from different task conditions and narrowband-filtered prior to applying GED.

Results

To validate the method, we simulated MEG data in two conditions with shared noise characteristics and unique signal. csGED outperformed the best sensor at reconstructing the ground truth signals, even in the presence of large amounts of noise. We next applied csGED to a published empirical MEG dataset on visual perception vs. imagery. csGED identified sources in alpha, beta, and gamma bands, and successfully separated distinct networks in the same frequency band.

Comparison with Existing Method(s)

GED is a flexible feature-guided decomposition method that has previously successfully been applied. Our combined frequency- and condition-tuning is a novel adaptation that extends the power of GED in cognitive electrophysiology.

Conclusions

We demonstrate successful condition-specific source separation by applying csGED to simulated and empirical data.

中文翻译：

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窄带多源分离用于实验对比的半盲发现

背景

电生理记录包含来自不同神经源的信号混合，这妨碍了对传感器级别数据的直接解释。当不同的源以重叠的频率谐振时，这种混合特别有害。幸运的是，混合是线性且瞬时的。因此，即使具有重叠的空间分布，多元源分离方法也可以成功地分离统计源。

新方法

我们演示了一种功能导向的多元源分离方法，该方法已针对窄带频率内容以及二进制条件差异进行了调整。这种方法-比较扫描广义特征分解csGED-利用多通道数据的协方差结构来找到最大程度地分离数据的两个子集的方向（即特征向量）。为了提高条件的特异性和频率的特异性，我们的数据子集来自不同的任务条件，并在应用GED之前进行了窄带滤波。

结果

为了验证该方法，我们在具有共享噪声特征和唯一信号的两个条件下模拟了MEG数据。即使在存在大量噪声的情况下，csGED在重建地面真相信号方面也优于最佳传感器。接下来，我们将csGED应用于已发布的关于视觉感知与图像的经验MEG数据集。csGED识别了alpha，beta和gamma波段的信号源，并成功地分离了同一频段中的不同网络。

与现有方法的比较

GED是一种灵活的功能导向分解方法，以前已成功应用。我们结合的频率和条件调谐是一种新颖的适应方法，扩展了GED在认知电生理学中的功能。

结论

通过将csGED应用于模拟和经验数据，我们证明了成功的针对特定条件的源分离。