立体脑电图
脑电图
信号(编程语言)
计算机科学
白质
人工智能
模式识别(心理学)
神经科学
心理学
癫痫外科
医学
磁共振成像
放射科
程序设计语言
作者
Guangye Li,Shize Jiang,Sivylla E. Paraskevopoulou,Meng Wang,Xu Yang,Zehan Wu,Liang Chen,Dingguo Zhang,Gerwin Schalk
出处
期刊:NeuroImage
[Elsevier]
日期:2018-12-01
卷期号:183: 327-335
被引量:96
标识
DOI:10.1016/j.neuroimage.2018.08.020
摘要
Stereo-electroencephalography (SEEG) is an intracranial recording technique in which depth electrodes are inserted in the brain as part of presurgical assessments for invasive brain surgery. SEEG recordings can tap into neural signals across the entire brain and thereby sample both cortical and subcortical sites. However, even though signal referencing is important for proper assessment of SEEG signals, no previous study has comprehensively evaluated the optimal referencing method for SEEG. In our study, we recorded SEEG data from 15 human subjects during a motor task, referencing them against the average of two white matter contacts (monopolar reference). We then subjected these signals to 5 different re-referencing approaches: common average reference (CAR), gray-white matter reference (GWR), electrode shaft reference (ESR), bipolar reference, and Laplacian reference. The results from three different signal quality metrics suggest the use of the Laplacian re-reference for study of local population-level activity and low-frequency oscillatory activity.
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