OBJECTIVE Visual inspection of interictal epileptiform discharges (IEDs) in multi-channel MEG requires a time-consuming evaluation process and often leads to inconsistent results due to variability of IED waveforms. Here, we propose a novel extraction method for IEDs using a T/k type of blind source separation (BSST/k). METHODS We applied BSST/k with seven patients with focal epilepsy to test the accuracy of identification of IEDs. We conducted comparisons of the results of BSS components with those obtained by visual inspection in sensor-space analysis. RESULTS BSST/k provided better signal estimation of IEDs compared with sensor-space analysis. Importantly, BSST/k was able to uncover IEDs that could not be detected by visual inspection. Furthermore, IED components were clearly extracted while preserving spike and wave morphology. Variable IED waveforms were decomposed into one dominant component. CONCLUSIONS BSST/k was able to visualize the spreading signals over multiple channels into a single component from a single epileptogenic zone. BSST/k can be applied to focal epilepsy with a simple parameter setting. SIGNIFICANCE Our novel method was able to highlight IEDs with increased accuracy for identification of IEDs from multi-channel MEG data.

中文翻译：

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一种提取多通道 MEG 发作间期癫痫样放电的新方法：使用分数型盲源分离

目标 在多通道 MEG 中对发作间期癫痫样放电 (IED) 进行目视检查需要耗时的评估过程，并且由于 IED 波形的可变性，通常会导致结果不一致。在这里，我们提出了一种使用 T/k 类型盲源分离 (BSST/k) 的 IED 提取方法。方法 我们对 7 名局灶性癫痫患者应用 BSST/k 来测试识别 IED 的准确性。我们将 BSS 组件的结果与在传感器空间分析中通过目视检查获得的结果进行了比较。结果 与传感器空间分析相比，BSST/k 提供了更好的 IED 信号估计。重要的是，BSST/k 能够发现目测无法检测到的 IED。此外，在保留尖峰和波形态的同时清楚地提取了 IED 组件。可变 IED 波形被分解为一种主要成分。结论 BSST/k 能够将通过多个通道的传播信号可视化为来自单个致癫痫区的单个成分。BSST/k 可以通过简单的参数设置应用于局灶性癫痫。意义我们的新方法能够突出显示具有更高准确性的 IED，以便从多通道 MEG 数据中识别 IED。