摘要
由一名新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-人工智能的新研究是一份新报告的主题。根据NewsRx记者来自O Saka大学的新闻报道，研究表明：“动态模式(DM)de合成将时空信号分解为基本振荡分量(DMs)。”这项研究的资助者包括Mext|日本科学技术厅；Ja Pan医学研究开发厅；Mext|日本科学促进协会。我们的新闻记者从大阪大学的研究中获得了一句话：“与传统的功率特征相比，DMs在非线性Grassmann核的情况下可以提高神经解码的准确性。然而，这种基于核的机器学习算法有三个局限性：计算量大，无法实时应用，与非核算法不兼容，以及可解释性低。这里，本文提出了一个对应于Grassmann核的映射函数,将DMs显式地转化为空间DM(sDM)特征,该特征可用于任何机器学习算法,并利用在各种运动和视觉任务中记录的脑电信号,证明了SDM特征与传统方法相比,提高了解码精度和计算时间.sDM的组成部分为解码提供了信息，显示出与信号的高G功率相似的特征，但具有更高的试验到试验的重复性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on artificial intelligenc e is the subject of a new report. According to news reporting originating from O saka University by NewsRx correspondents, research stated, “Dynamic mode (DM) de composition decomposes spatiotemporal signals into basic oscillatory components (DMs).” Funders for this research include Mext | Japan Science And Technology Agency; Ja pan Agency For Medical Research And Development; Mext | Japan Society For The Pr omotion of Science. Our news correspondents obtained a quote from the research from Osaka University : “DMs can improve the accuracy of neural decoding when used with the nonlinear Grassmann kernel, compared to conventional power features. However, such kernel- based machine learning algorithms have three limitations: large computational ti me preventing real-time application, incompatibility with non-kernel algorithms, and low interpretability. Here, we propose a mapping function corresponding to the Grassmann kernel that explicitly transforms DMs into spatial DM (sDM) featur es, which can be used in any machine learning algorithm. Using electrocorticogra phic signals recorded during various movement and visual perception tasks, the s DM features were shown to improve the decoding accuracy and computational time c ompared to conventional methods. Furthermore, the components of the sDM features informative for decoding showed similar characteristics to the high-g power of the signals, but with higher trial-to-trial reproducibility.”
NETL
NSTL