计算机科学
协议(科学)
工具箱
电生理学
脑磁图
软件
人工智能
数据提取
脑电图
模式识别(心理学)
机器学习
神经科学
生物
病理
程序设计语言
替代医学
医学
梅德林
生物化学
作者
Arjen Stolk,Sandon Griffin,Roemer van der Meij,Callum Dewar,Ignacio Sáez,Jack J. Lin,Giovanni Piantoni,Jan‐Mathijs Schoffelen,Robert T. Knight,Robert Oostenveld
出处
期刊:Nature Protocols
[Springer Nature]
日期:2018-07-01
卷期号:13 (7): 1699-1723
被引量:131
标识
DOI:10.1038/s41596-018-0009-6
摘要
Human intracranial electroencephalography (iEEG) recordings provide data with much greater spatiotemporal precision than is possible from data obtained using scalp EEG, magnetoencephalography (MEG), or functional MRI. Until recently, the fusion of anatomical data (MRI and computed tomography (CT) images) with electrophysiological data and their subsequent analysis have required the use of technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that enables complex raw human iEEG data to be converted into more readily comprehensible illustrative representations. The protocol uses an open-source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and used by a large research community. In this protocol, we describe how to analyze complex iEEG datasets by providing an intuitive and rapid approach that can handle both neuroanatomical information and large electrophysiological datasets. We provide a worked example using an example dataset. We also explain how to automate the protocol and adjust the settings to enable analysis of iEEG datasets with other characteristics. The protocol can be implemented by a graduate student or postdoctoral fellow with minimal MATLAB experience and takes approximately an hour to execute, excluding the automated cortical surface extraction.
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