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基于重采样的神经技术设计与评价方法

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: 丹麦工业大学
开课时间: 2012-12-03
课程语种: 英语
中文简介:
通过PET、MR、EEG和MEG进行脑成像已经成为系统级神经科学的基石。神经影像数据集的统计分析面临许多有趣的挑战,包括非线性和多尺度的时空动力学。神经影像学的目标是双重的,我们感兴趣的是最精确的,即预测的,统计的模型,但同样重要的是模型解释和可视化,通常采取“大脑映射”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,并提出一个基于计算机密集型数据重采样方案的模型评估、解释和可视化的通用框架。在此框架内,我们得到了一个无偏估计的预测性能和可靠性的脑图可视化。
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: 神经科学; 脑图可视化; 脑成像
课程来源: 视频讲座网
数据采集: 2020-12-29:yxd
最后编审: 2020-12-29:yxd
阅读次数: 32