Acar Zeynep Akalin, Makeig Scott, Worrell Gregory
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California-San Diego, La Jolla, CA, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:3763-6. doi: 10.1109/IEMBS.2008.4650027.
In this study, we developed numerical methods for investigating the dynamics of epilepsy from multi-scale EEG recordings acquired simultaneously from the scalp (sEEG) and intracranial subdural and/or depth electrodes (iEEG) in patients undergoing pre-surgical evaluation at the epilepsy center of the Mayo Clinic (Rochester, MN). The data are analyzed using independent component analysis (ICA), which identifies and isolates independent signal components from multi-channel recordings. A realistic individual head model was constructed for a patient undergoing pre-surgical evaluation. The forward problem of electro-magnetic source localization was solved using the Boundary Element Method (BEM). Using this approach, we investigated the relationships between noninvasive and invasive source localization of human electrical brain data sources. A difference of about 1 cm was observed between sources estimated from sEEG and iEEG measurements.
在本研究中，我们开发了数值方法，用于研究梅奥诊所（明尼苏达州罗切斯特）癫痫中心接受术前评估的患者，从头皮（sEEG）以及颅内硬膜下和/或深度电极（iEEG）同时采集的多尺度脑电图记录中的癫痫动态。使用独立成分分析（ICA）对数据进行分析，该方法可从多通道记录中识别并分离出独立的信号成分。为一名接受术前评估的患者构建了逼真的个体头部模型。使用边界元法（BEM）解决电磁源定位的正向问题。通过这种方法，我们研究了人类脑电数据源的非侵入性和侵入性源定位之间的关系。在根据sEEG和iEEG测量估计的源之间观察到约1厘米的差异。
