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信息脑思维特征空间

Informative Brain-Mind Feature Space
课程网址: http://videolectures.net/bbci2012_cohen_feature_space/  
主讲教师: Mark Cohen
开课单位: 加州大学洛杉矶分校
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:
目前有各种各样的脑源信号用于驱动大脑计算机接口设备。其中包括流行的脑电图记录、脑磁图、功能性磁共振成像(fMRI)、近红外光谱等。众所周知,每一个都被有意识的心理活动定量地改变,在统计机器学习提供的力量下,每一个都可以在不同程度上被解码到控制装置的末端。我会说的反向挑战的问题更好的理解大脑的操作控制信号的分析和认为自己可能的仔细选择特性的双重目的,提高脑机接口的效率和准确性,并有助于改善我们的理解潜在的神经生理学。讨论将集中在通过fMRI暴露的大脑网络特征的使用,以及了解个体特征及其状态转换的时间动态。
课程简介: A wide variety of brain-derived signals presently are available to drive brain computer interface devices. These include the popular EEG recordings, magnetoencephalography, functional magnetic resonance imaging (fMRI), near-infrared spectroscopy and others. Each are known to be quantitatively altered by intentional mental activity and, with the power provided by statistical machine learning, each to varying degrees may be decoded to the end of controlling devices. I will speak to the question of the reverse challenge of understanding better the operations of the brain through analysis of the control signals and argue that careful selection of the features themselves might serve the dual purposes of improving the efficiency and accuracy of the brain-computer interface, and serve to improve our understanding of the underlying neurophysiology. The discussion will focus on the use of brain network features exposed through fMRI and on understanding the temporal dynamics of the individual features and their state transitions.
关 键 词: 信息; 脑思维; 特征; 空间
课程来源: 视频讲座网
最后编审: 2020-06-06:刘家豪(课程编辑志愿者)
阅读次数: 77