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基于重采样的方法与评价技术设计

Resampling Based Methods for Design and Evaluation of Neurotechnology
课程网址: http://videolectures.net/bbci2012_hansen_neurotechnology/  
主讲教师: Lars-Kai Hansen
开课单位: 丹麦技术大学
开课时间: 信息不详。欢迎您在右侧留言补充。
课程语种: 英语
中文简介:
PET、MR、EEG和MEG脑成像已经成为系统级神经科学的基石。神经图像数据的统计分析面临着许多有趣的挑战,包括非线性和多尺度时空动力学。神经成像的目标是双重的,我们对最精确的,即预测性的统计模型感兴趣,但是同样重要的是模型解释和可视化,它们通常采取“ld.;.mapping&rd.;”的形式。我将介绍一些当前用于探索性和假设驱动的神经图像建模的机器学习策略,并基于计算机密集型数据重采样方案提出一个通用的模型评估、解释和可视化框架。在这个框架中,我们既获得了预测性能的无偏估计,也获得了脑地图可视化的可靠性。
课程简介: Brain imaging by PET, MR, EEG, and MEG has become a cornerstone in systems level neuroscience. Statistical analyses of neuroimage datasets face many interesting challenges including non-linearity and multi-scale spatial and temporal dynamics. The objectives of neuroimaging are dual, we are interested in the most accurate, i.e., predictive, statistical model, but equally important is model interpretation and visualization which often takes the form of “brain mapping”. I will introduce some current machine learning strategies invoked for explorative and hypothesis driven neuroimage modeling, and present a general framework for model evaluation, interpretation, and visualization based on computer intensive data re-sampling schemes. Within the framework we obtain both an unbiased estimate of the predictive performance and of the reliability of the brain map visualization.
关 键 词: 神经影像数据; 统计模型; 脑图可视化
课程来源: 视频讲座网
最后编审: 2019-11-17:cwx
阅读次数: 38