瞬态(计算机编程)
脑电图
振幅
联轴节(管道)
相(物质)
静息状态功能磁共振成像
国家(计算机科学)
计算机科学
物理
材料科学
神经科学
心理学
光学
算法
量子力学
冶金
操作系统
作者
Alper Er,Michel Le Van Quyen,Julien Dauguet,Véronique Marchand‐Pauvert,Guillaume Marrelec
标识
DOI:10.1109/embc53108.2024.10782859
摘要
Electroencephalography (EEG) allows us to observe brain activity through electrical signals. Phase-amplitude coupling (PAC) is a way to analyze EEG data by focusing on the interaction between the low- and high-frequency components of these signals. However, PAC analyses are often challenged by various methodological issues. We here propose a novel approach which alleviates these issues. Our method has the following features: (i) it addresses the transient nature of coupling through data epoching; (ii) it ensures the presence of low-frequency oscillations through peak detection in the power spectrum; (iii) it applies adaptive high-frequency filtering; and (iv) it performs statistical validation using surrogate data. The efficiency of our method is demonstrated through both a simulation study and the analysis of experimental EEG data, offering new insights into the intricate workings of brain signal interactions.
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