Acar Zeynep Akalin, Ortiz-Mantilla Silvia, Benasich April, Makeig Scott
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:117-120. doi: 10.1109/EMBC.2016.7590654.
Recently we described an iterative skull conductivity and source location estimation (SCALE) algorithm for simultaneously estimating head tissue conductivities and brain source locations. SCALE uses a realistic FEM forward problem head model and scalp maps of 10 or more near-dipolar sources identified by independent component analysis (ICA) decomposition of sufficient high-density EEG data. In this study, we applied SCALE to 20 minutes of 64-channel EEG data and magnetic resonance (MR) head images from four twelve-months-of-age infants. For each child, we selected 15-16 near-dipolar independent components from multiple-model adaptive mixture ICA (AMICA) decomposition of their EEG data. SCALE converged to brain-to-skull conductivity ratio (BSCR) estimates in the 10-12 range and mostly compact gyral or sulcal cortical distributions for the IC sources.
最近，我们描述了一种迭代颅骨电导率和源位置估计（SCALE）算法，用于同时估计头部组织电导率和脑源位置。SCALE使用逼真的有限元法（FEM）正向问题头部模型以及通过对足够高密度脑电图（EEG）数据进行独立成分分析（ICA）分解识别出的10个或更多近偶极源的头皮图。在本研究中，我们将SCALE应用于来自四名12个月大婴儿的20分钟64通道EEG数据和磁共振（MR）头部图像。对于每个孩子，我们从其EEG数据的多模型自适应混合ICA（AMICA）分解中选择了15 - 16个近偶极独立成分。SCALE收敛到10 - 12范围内的脑与颅骨电导率比（BSCR）估计值，并且IC源大多具有紧密的脑回或脑沟皮质分布。
