貝氏估計在事件相關電位之大腦訊息源造影應用

10.29973/JCSA.200406.0001

蔡志鑫(Arthur C. Tsai)；劉長萱(Michelle Liou)；程爾觀(Philip E. Cheng)

訊息源定位／造影 ； 獨立成份分析 ； 貝氏估計模型 ； Bayesian spatiotemporal analysis ； event-related potentials ； ICA ； inverse problem

中國統計學報

42卷2期（2004 / 06 / 01）

99 - 114

繁體中文

腦電(EEG; electroencephalographic)及功能性磁振造影(fMRI; functional magnetic resonance imaging)是目前研究大腦神經功能及訊息傳導的主要工具。不同技術有其獨特優點與限制；腦電的時變解析力(temporal resolution)可達毫秒，在研究大腦訊息傳導上有深厚的潛力。但腦電空間解析力(spatial resolution)較差；一般在進行事件相關電位實驗(ERP; event-related potential)之訊息源造影時，會將相同實驗情況所得之腦電資料平均，藉此將雜訊去除。然而部份分離法可以發現在部份認知實驗中將腦電平均之後的波形，其波峰很多是由於實驗刺激後的相位重設(phase resetting)造成，並非事件相關的訊息源。因此採事件相關電位資料平均法(ERP average)恐難窺探真正與實驗相關且有意義的成份。為得到更精確認知相關的神經系統動，事件相關電位分析應同時包含成份分離及訊息源造影兩件工作。本研究討論過去貝氏估計模型在訊息源定位／造影之應用，並以貝氏構創新提出另一種可以同時將獨立成份分離並將其事件相關成份作大腦功能造影。

Electroencephalography (EEG) and functional magnetic resonance images (fMRI) have been important methodologies for research into human brain function. In this study, we reviewed methods of mapping event-related neural generators on the cortical surface based on EEG data and MR brain images. We also briey introduced a statistical framework for estimating temporal independent neural generators and their cortical location simultaneously. The use of the approach was illustrated in an empirical study.

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