Institute of Cognitive Neuroscience, National Central University, Taoyuan City 320317, Taiwan.
Sensors (Basel). 2021 Sep 17;21(18):6235. doi: 10.3390/s21186235.
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.
神经解码可用于探索大脑编码信息的时间和源位置。更高的分类准确性意味着分析更有可能成功地从噪声中提取有用信息。在本文中，我们介绍了一种非线性、非平稳信号分解技术——经验模态分解（EMD）在 MEG 数据中的应用。我们讨论了在分析脑电波信号时使用非线性方法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展示了该过程。数据清晰度的提高允许进一步的解码分析捕捉到以前在现有文献中被忽视的条件之间的区别特征，同时提出了有关大脑半球优势对面部和身份信息编码过程的有趣问题。
