Univ. Grenoble Alpes, CEA, LETI, CLINATEC, MINATEC Campus, F-38000 Grenoble, France.
CHU Grenoble Alpes, CLINATEC, F-38000 Grenoble, France.
Sensors (Basel). 2020 May 9;20(9):2706. doi: 10.3390/s20092706.
Brain source imaging and time frequency mapping (TFM) are commonly used in magneto/electro encephalography (M/EEG) imaging. However, these methods suffer from important limitations. Source imaging is based on an ill-posed inverse problem leading to instability of source localization solutions, has a limited capacity to localize high frequency oscillations and loses its robustness for induced responses (ill-defined trigger). The drawback of TFM is that it involves independent analysis of signals from a number of frequency bands, and from co-localized sensors. In the present article, a regression-based multi-sensor space-time-frequency analysis (MSA) approach, which integrates co-localized sensors and/or multi-frequency information, is proposed. To estimate task-specific brain activations, MSA uses cross-validated, shifted, multiple Pearson correlation, calculated from the time-frequency transformed brain signal and the binary signal of stimuli. The results are projected from the sensor space onto the cortical surface. To assess MSA performance, the proposed method was compared to the weighted minimum norm estimate (wMNE) source imaging method, in terms of spatial selectivity and robustness against an ill-defined trigger. Magnetoencephalography (MEG) recordings were performed in fourteen subjects during two motor tasks: finger tapping and elbow flexion/extension. In particular, our results show that the MSA approach provides good localization performance when compared to wMNE and statistically significant improvement of robustness against ill-defined trigger.
脑源成像和时频映射（TFM）常用于脑磁图/脑电图（M/EEG）成像。然而，这些方法存在重要的局限性。源成像基于病态逆问题，导致源定位解不稳定，对高频振荡的定位能力有限，对诱发反应的鲁棒性降低（定义不明确的触发）。TFM 的缺点是它涉及对来自多个频带和共定位传感器的信号进行独立分析。在本文中，提出了一种基于回归的多传感器时空频率分析（MSA）方法，该方法集成了共定位传感器和/或多频信息。为了估计特定任务的大脑激活，MSA 使用交叉验证、移位、多个 Pearson 相关系数，从时频变换后的脑信号和刺激的二进制信号中计算得到。结果从传感器空间投影到皮质表面。为了评估 MSA 的性能，将所提出的方法与加权最小范数估计（wMNE）源成像方法进行了比较，从空间选择性和对定义不明确的触发的鲁棒性方面进行了比较。在十四名受试者进行了两项运动任务：手指敲击和肘部弯曲/伸展的过程中进行了脑磁图（MEG）记录。特别是，我们的结果表明，与 wMNE 相比，MSA 方法在定位性能方面表现良好，并且在对定义不明确的触发的鲁棒性方面有显著的提高。
