穩健性腦磁波訊號源造影

Abstract

為了探索人腦功能，腦部訊號源造影是必須的。腦磁波儀可以非侵入式地量測腦波訊號，並且具有優越的時序解析度和高訊雜比。我們可以藉由腦磁波儀所提供的腦波訊號時間及空間資訊來達到腦部功能探索的目的。 腦部訊號源造影有很多相關的議題，例如腦磁波儀所擷取到的腦波訊號量、何者為適當的訊號分析以及訊號源造影、以及一些影響訊號源估測的因素。我們提出了一些方法來解決上列的問題。首先，我們利用統計檢定的方法，顯示活化狀態和控制狀態的差異性。藉由顯示兩者的差異程度，當達到顯著差異的感應器數量夠多時，我們可以得到一個判斷擷取到的訊號量已經足夠的依據。 第二，為了達到訊號分析的目的，獨立成分分析是一個很強大的方法，它可以將線性組合的訊號分解成獨立成分。然而，用獨立成分分析時，我們通常會觀察每個獨立成分的時序活動狀態和腦部地形圖的分佈狀態。當輸入訊號是腦磁波儀量測到的腦波訊號時，腦部地形圖的解析度就會是在感應器層，我們希望提升到斷層掃瞄的解析度。 許多演算法被提出來為了解決訊號源位置估測的問題。以光束構成法為基礎之空間濾波法進行腦磁波活動源估測近年來受到矚目。此法主要的優點在於針對某一位置估算其腦部活動訊號時，能在維持其活動強度的條件下同時抑制其他訊號源對估算所產生的影響。以最大對比為限制之改良式腦磁波光束構成法是一個新穎的方法，利用最大化活化狀態和控制狀態的變異數之對比估算出一個空間濾波器的最佳方向。但是光束構成法最大的問題就是有很多需要調整的參數，尤其是正規化參數。不適合的正規化參數會導致不準確的空間濾波器。 我們結合了獨立成分分析和以最大對比為限制之改良式腦磁波光束構成法，稱之為以光束構成法為基礎的獨立成分分析。將以最大對比為限制之改良式腦磁波光束構成法得到的空間濾波器濾出的訊號當作獨立成分分析的輸入訊號。因此，一些因為不適合的正規化參數所造成的漏電流，可以藉著獨立成分分析將其分離。再者，我們可以藉由光束構成法之虛擬感應器的觀念來提升獨立成分分析的結果達到斷層掃瞄的解析度。 第三，仍然有一些會影響訊號源位置估測的因素，例如在腦磁波儀實驗中的頭部移動，很多演算法提出來為了解決這個問題。其中穩定線性模型不僅可以解決頭部移動的問題，同時也因為具有虛擬增加感應器的能力而提升了訊號源位置估測的正確性。 模擬訊號、假體和實際實驗的訊號同時用來檢測以上方法的正確性。我們使用配對t統計來檢測活化和控制狀態訊號的差異度，藉由這個結果，我們可以設定一個評估訊號量是否足夠的準則。再者，以光束構成法為基礎的獨立成分分析的優點也經由實驗被證實。最後，將穩定線性模型套入以最大對比為限制之改良式腦磁波光束構成法，的確可以解決頭部移動的問題；並藉由結合各種不同頭部位置的訊號，虛擬地增加感應器數量，而得到更準確的訊號源位置估測結果。

To discover brain functionalities, we need to do brain source imaging. Magnetoencephalography (MEG) can record brain signals non-invasively with superior temporal resolution and high Signal-to-Noise Ratio (SNR). There are some issues about magnetic source imaging, like the amount of MEG recordings, the appropriate approach for signal analysis and source localization and head motion correction. We propose some methods to overcome these difficulties. First, we use statistical hypothesis test to reveal the discrimination between the selected active and baseline states. By revealing their discrimination, we can set an evaluation criterion for the amount of recordings. Second, in order to conduct signal analysis, Independent Component Analysis (ICA) is an powerful method which can retrieve independent components from the linear mixture of sources, under the assumption that components are statistical independent. However, the resolution of ICA topography distribution is in sensor space. If we want to achieve the goal of source imaging, it is necessary to raise the resolution to tomography distribution in source space . Many algorithms are proposed to solve the problem of source localization. In recent years, the most attracting method is Beamforming. It can obtain the activation magnitude of the targeted source by imposing the unit-gain constraint while suppressing the contribution from other sources by applying the minimum variance criterion. Maximum Contrast Beamformer (MCB) is an outstanding method that it can estimate the orientation of the spatial filter from an analytical form which is generated from maximizing the contrast of active state and control state. However, the major problems in beamformer are the parameters needed to be well-selected, especially the regularization parameter. The inappropriate value of the regularization parameter can result in inaccurate spatial filter estimation. We get more accurate solution to source localization by combining ICA with MCB, which is called Beamformer-based ICA.We consider filtered signals form the MCB spatial filter as the output from the virtual sensor at each voxel of interest and take them as the ICA input signals. As a result, with inappropriate regularization parameter of beamfermer, we can use ICA to separate leakage from target source. Furthermore, we can raise ICA from topography distribution to tomography distribution by the virtual sensor concept of beamformer. Third, there are still some factors that will affect the accuracy of source localization, like the head motion during MEG experiments. This is unavoidable during MEG studies with long signal recording time. Many algorithms are proposed to solve this problem. With Stabilized Linear Model (SLIM), we can virtually increase the sensor number of MCB model to reduce the localization error induced from head motion and improve the accuracy of source localization simultaneously. Experiments with simulation and real data are used to validate the proposed methods. According to the experiments, by revealing the discrimination between the selected active and baseline states, we can set an evaluation criterion for the amount of recordings. Moreover, as aforementioned, the expected advantages of Beamformer-based ICA are demonstrated with simulation data. Applying SLIM into MCB is also validated that it is able to reduce the localization error induced from head motion and improve the accuracy of source localization by combining the different head poses.