Real-time detection of event-related brain activity.

Real-time detection of event-related brain activity.
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DOI:
10.1016/j.neuroimage.2008.07.037
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发表时间:
2008-11-01
期刊:
影响因子:
5.7
通讯作者:
Wolpaw JR
Wolpaw JR
中科院分区:
医学1区
文献类型:
--
作者:
Schalk G;Leuthardt EC;Brunner P;Ojemann JG;Gerhardt LA;Wolpaw JR

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大脑信号的复杂性和个体间的差异阻碍了对原始信号中事件的实时检测。为了将这些复杂的信号转换成易于理解的结果,目前的方法通常在收集完所有数据后,对来自已知条件的数据应用统计方法。能够提供复杂的大脑信号的有意义的可视化,而不需要最初从所有条件下收集数据,这将提供一种新的工具,本质上是一种新的成像技术,将为大脑功能的研究开辟新的途径。在这里,我们展示了一种新的分析方法,称为西格弗里德,可以克服当前方法的严重局限性。西格弗里德可以可视化大脑信号的变化,而不需要事先从所有情况下收集数据。这种能力特别适合于不可能或不切实际地进行全面的先前数据收集的应用,例如术中皮质功能的定位或癫痫发作的检测。
The complexity and inter-individual variation of brain signals impedes real-time detection of events in raw signals. To convert these complex signals into results that can be readily understood, current approaches usually apply statistical methods to data from known conditions after all data have been collected. The capability to provide meaningful visualization of complex brain signals without the requirement to initially collect data from all conditions would provide a new tool, essentially a new imaging technique, that would open up new avenues for the study of brain function. Here we show that a new analysis approach, called SIGFRIED, can overcome this serious limitation of current methods. SIGFRIED can visualize brain signal changes without requiring prior data collection from all conditions. This capacity is particularly well suited to applications in which comprehensive prior data collection is impossible or impractical, such as intraoperative localization of cortical function or detection of epileptic seizures.
DOI: 10.1093/brain/awh491
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