多模态神经影像的机器学习Machine Learning for Multimodal Neuroimaging |
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课程网址: | http://videolectures.net/bbci2014_biessmann_machine_learning/ |
主讲教师: | Felix Bießmann |
开课单位: | 德国柏林工业大学 |
开课时间: | 2014-04-03 |
课程语种: | 英语 |
中文简介: | 多种神经影像学方法的结合已成为一个重要的研究领域。虽然与多模态神经影像学相关的技术挑战在十多年前就已经掌握,但多模态神经影像资料的分析技术仍在发展中。本教程将涵盖多模式神经成像的数据驱动分析技术,包括多模式脑-计算机接口的最新进展,以及神经带功率信号与血流动力学信号的整合。我们将特别关注简单有效的子空间方法,这些方法在多模态神经影像分析的所有阶段都是有用的,从基本的预处理和伪影消除到具有复杂时空耦合动力学的多模态集成。 |
课程简介: | The combination of multiple neuroimaging modalities has become an important field of research. While the technical challenges associated with multimodal neuroimaging have been mastered more than a decade ago, analysis techniques for multimodal neuroimaging data are still being developed. This tutorial will cover data driven analysis techniques for multimodal neuroimaging, including recent advances in multimodal brain-computer-interfaces and in integration of neural bandpower signals with hemodynamic signals. A special focus will be placed on simple and efficient subspace methods that are useful in all stages of multimodal neuroimaging analyses, starting from basic preprocessing and artifact removal to integration of multiple modalities with complex spatiotemporal coupling dynamics. |
关 键 词: | 神经; 机器学习; 药物 |
课程来源: | 视频讲座网 |
数据采集: | 2020-12-14:yxd |
最后编审: | 2020-12-14:yxd |
阅读次数: | 86 |