NEUROMAGNETOMETER COMPUTER SYSTEM

NINCDS的医学神经病学分支、生物医学工程和 DRS仪器分支和DCRT计算机系统实验室 正在合作进行一项研究项目， 利用神经磁记录的人脑内的放电源 结合常规脑电图（EEG）记录。 许多癫痫患者表现出低水平的细胞放电 在癫痫发作之间，由脑电图中的发作间期尖峰指示， 脑磁图（MEG）记录。 该项目涉及 发展计算机技术，以实现 目前NINCDS神经学家用于确定 癫痫患者发作间期棘波源的颅内定位 癫痫 所需的MEG和EEG信号是用一个系统采集的， 购自Biomagnetic Technologies，Inc. (BTi)并置于 在FY 86中运行。 该系统包括一个七通道SQUID 用于测量MEG信号的超导量子干涉装置 同时从头部的七个部位， 用于采集EEG和MEG的Hewlett-Packard（HP）数据采集系统 信号，以及用于信号处理的HP 9000计算机工作站， 图形显示功能。 CSL设计并建造了32通道信号选择和调理 数据采集系统中添加的接口。 方法 目前正在开发自动化的真实的时间检测发作间期 EEG和MEG信号的尖峰以及相关特征的提取 表征检测到的波形。 从这些信息中获得的信息 特征将与从原始波形中提取的值一起使用， 生成确定癫痫灶所需的图形显示。

The Medical Neurology Branch of NINCDS, the Biomedical Engineering and Instrumentation Branch of DRS, and the Computer Systems Laboratory of DCRT are collaborating on a research project to noninvasively localize epileptic discharge sources within the human brain utilizing neuromagnetic recording in conjunction with conventional electroencephalogram (EEG) recording. Many patients with seizure disorders exhibit low-level cellular discharges between seizures, indicated by interictal spikes in their EEG and magnetoencephalogram (MEG) recordings. This project involves the development of computer techniques for automating and enhancing the procedure that is presently used by NINCDS neurologists for determining the intracranial locations of the sources of interictal spikes in patients with epilepsy. The required MEG and EEG signals are acquired with a system that was purchased from Biomagnetic Technologies, Inc. (BTi) and placed in operation during FY86. This system includes a seven-channel SQUID (superconducting quantum interference device) for measuring the MEG signal from seven sites on the head simultaneously, a combined BTi and Hewlett-Packard (HP) data acquisition system for acquiring the EEG and MEG signals, and an HP 9000 computer workstation for signal processing and graphical display functions. CSL designed and built a 32-channel signal selection and conditioning interface that was added to the data acquisition system. Methods are presently being developed to automate the real time detection of interictal spikes from the EEG and MEG signals and the extraction of relevant features characterizing the detected waveforms. The information obtained from these features will be used with values extracted from the raw waveforms to generate the graphical displays needed to determine the epileptic foci.