Neural decoding is useful to explore the timing and source location in which the brain encodes information. Higher classification accuracy means that an analysis is more likely to succeed in extracting useful information from noises. In this paper, we present the application of a nonlinear, nonstationary signal decomposition technique—the empirical mode decomposition (EMD), on MEG data. We discuss the fundamental concepts and importance of nonlinear methods when it comes to analyzing brainwave signals and demonstrate the procedure on a set of open-source MEG facial recognition task dataset. The improved clarity of data allowed further decoding analysis to capture distinguishing features between conditions that were formerly over-looked in the existing literature, while raising interesting questions concerning hemispheric dominance to the encoding process of facial and identity information.

中文翻译：

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经验模态分解在脑磁图人脸感知解码中的应用

神经解码对于探索大脑编码信息的时间和源位置很有用。更高的分类准确度意味着分析更有可能成功地从噪声中提取有用的信息。在本文中，我们介绍了非线性、非平稳信号分解技术——经验模式分解 (EMD) 在 MEG 数据上的应用。我们讨论了非线性方法在分析脑波信号时的基本概念和重要性，并在一组开源 MEG 面部识别任务数据集上演示了该过程。数据清晰度的提高允许进一步解码分析，以捕获以前在现有文献中被忽视的条件之间的区别特征，