标题： 数学专家与初学者高密度脑电数据的非线性与机器学习分析 显示英文标题

标题： Nonlinear and Machine Learning Analyses on High-Density EEG data of Math Experts and Novices

摘要： 神经科学领域的当前趋势是使用自然主义刺激，例如电影、课堂生物学或电子游戏，旨在理解在生态有效的条件下大脑功能的运作。自然主义刺激招募了复杂的且相互重叠的认知、情感和感觉脑过程。脑振荡构成了这些过程的基础机制，并且这些过程可以通过专业知识进行修改。尽管大脑作为一个生物系统高度非线性，但人类皮质振荡通常使用线性方法进行分析。本研究应用了一种相对稳健的非线性方法——Higuchi分形维数（HFD），来分类数学专家与新手在EEG实验室中解决长且复杂的数学证明时的大脑皮层振荡。在自然主义刺激下收集的长时间跨度的脑成像数据，使得数据驱动的分析得以应用。因此，我们还利用机器学习算法探索了数学专长的神经特征。由于基于简约化和简化研究设计的世界实际大脑功能理论的制定既具有挑战性又值得质疑，因此需要新的方法来分析自然主义数据。数据驱动的智能方法可能有助于开发和测试关于复杂大脑功能的新理论。我们的结果阐明了由HFD分析的数学专家与新手在复杂数学中的不同神经特征，并建议机器学习作为一种有前景的数据驱动方法来理解专长和数学认知中的大脑过程。 显示英文摘要

摘要： Current trend in neurosciences is to use naturalistic stimuli, such as cinema, class-room biology or video gaming, aiming to understand the brain functions during ecologically valid conditions. Naturalistic stimuli recruit complex and overlapping cognitive, emotional and sensory brain processes. Brain oscillations form underlying mechanisms for such processes, and further, these processes can be modified by expertise. Human cortical oscillations are often analyzed with linear methods despite brain as a biological system is highly nonlinear. This study applies a relatively robust nonlinear method, Higuchi fractal dimension (HFD), to classify cortical oscillations of math experts and novices when they solve long and complex math demonstrations in an EEG laboratory. Brain imaging data, which is collected over a long time span during naturalistic stimuli, enables the application of data-driven analyses. Therefore, we also explore the neural signature of math expertise with machine learning algorithms. There is a need for novel methodologies in analyzing naturalistic data because formulation of theories of the brain functions in the real world based on reductionist and simplified study designs is both challenging and questionable. Data-driven intelligent approaches may be helpful in developing and testing new theories on complex brain functions. Our results clarify the different neural signature, analyzed by HFD, of math experts and novices during complex math and suggest machine learning as a promising data-driven approach to understand the brain processes in expertise and mathematical cognition.