Michel Christoph M, Brunet Denis
Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland.
Center for Biomedical Imaging Lausanne-Geneva (CIBM), Geneva, Switzerland.
Front Neurol. 2019 Apr 4;10:325. doi: 10.3389/fneur.2019.00325. eCollection 2019.
The electroencephalogram (EEG) is one of the oldest technologies to measure neuronal activity of the human brain. It has its undisputed value in clinical diagnosis, particularly (but not exclusively) in the identification of epilepsy and sleep disorders and in the evaluation of dysfunctions in sensory transmission pathways. With the advancement of digital technologies, the analysis of EEG has moved from pure visual inspection of amplitude and frequency modulations over time to a comprehensive exploration of the temporal and spatial characteristics of the recorded signals. Today, EEG is accepted as a powerful tool to capture brain function with the unique advantage of measuring neuronal processes in the time frame in which these processes occur, namely in the sub-second range. However, it is generally stated that EEG suffers from a poor spatial resolution that makes it difficult to infer to the location of the brain areas generating the neuronal activity measured on the scalp. This statement has challenged a whole community of biomedical engineers to offer solutions to localize more precisely and more reliably the generators of the EEG activity. High-density EEG systems combined with precise information of the head anatomy and sophisticated source localization algorithms now exist that convert the EEG to a true neuroimaging modality. With these tools in hand and with the fact that EEG still remains versatile, inexpensive and portable, electrical neuroimaging has become a widely used technology to study the functions of the pathological and healthy human brain. However, several steps are needed to pass from the recording of the EEG to 3-dimensional images of neuronal activity. This review explains these different steps and illustrates them in a comprehensive analysis pipeline integrated in a stand-alone freely available academic software: Cartool. The information about how the different steps are performed in Cartool is only meant as a suggestion. Other EEG source imaging software may apply similar or different approaches to the different steps.
脑电图(EEG)是测量人类大脑神经元活动最古老的技术之一。它在临床诊断中具有无可争议的价值,尤其是(但不限于)在癫痫和睡眠障碍的识别以及感觉传导通路功能障碍的评估方面。随着数字技术的进步,脑电图分析已从单纯的随时间对幅度和频率调制进行视觉检查,发展到对记录信号的时空特征进行全面探索。如今,脑电图被公认为是一种强大的工具,用于捕捉大脑功能,其独特优势在于能够在神经元活动发生的时间范围内(即亚秒级范围内)测量神经元过程。然而,一般认为脑电图的空间分辨率较差,这使得难以推断头皮上测量到的神经元活动所产生的脑区位置。这一观点促使整个生物医学工程师群体寻求解决方案,以更精确、更可靠地定位脑电图活动的产生源。现在已有高密度脑电图系统,结合头部解剖结构的精确信息和复杂的源定位算法,将脑电图转变为一种真正的神经成像模式。有了这些工具,再加上脑电图仍然具有通用性、低成本和便携性的特点,电神经成像已成为研究人类病理和健康大脑功能的广泛应用技术。然而,从脑电图记录到神经元活动的三维图像还需要几个步骤。这篇综述解释了这些不同步骤,并在一个独立的免费学术软件Cartool中集成的综合分析流程中进行了说明。关于在Cartool中如何执行不同步骤的信息仅作为参考。其他脑电图源成像软件在不同步骤中可能采用类似或不同的方法。