跳到主要內容

臺灣博碩士論文加值系統

(216.73.216.213) 您好!臺灣時間:2025/11/08 00:02
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:劉柏廷
研究生(外文):Po-Ting Bertram Liu
論文名稱:腦磁圖中空間擴展且完全相關的活動源對空間濾波器的影響──以聽覺穩態反應為例
論文名稱(外文):The effect of fully correlated sources with spatial extents on spatial filtering on the MEG data - A study of Auditory Steady-State Response
指導教授:胡竹生胡竹生引用關係
指導教授(外文):Hu, Jwu-Sheng
口試委員:陳永昇楊谷洋
口試委員(外文):Chen, Yong-ShengYoung, Kuu-Young
口試日期:2019-07-08
學位類別:碩士
校院名稱:國立交通大學
系所名稱:工學院聲音與音樂創意科技碩士學位學程
學門:工程學門
學類:其他工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:45
中文關鍵詞:腦磁圖空間擴展相關的活動源活動源造影空間濾波器
外文關鍵詞:MEGSpatial extentCorrelated sourcesSource imagingSpatial filering
相關次數:
  • 被引用被引用:0
  • 點閱點閱:277
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本篇論文探討聽覺穩態反應的腦磁圖活動源造影的問題,因為給受試者的兩耳相似的聽覺刺激,其腦波訊號特性經常會有完全相關的神經活動。在傳統的空間濾波器,因為其假設活動源都是不相關的,所以無法估算正確的完全相關活動源,本篇論文研究方法主要採用雙核波束成形器,目前已有不少論文討論其特性與限制,唯獨完全相關活動源的空間擴展未被納入討論。在模擬實驗中,分別探究各種雜訊活動源與完全相關活動源的空間擴展對DCBF的定位影響。從本篇論文的結果,可發現定位器NAI相較定位器K可找出正確的位置,定位器NAI在空間擴展的標準差5 mm以下都不會影響其定位準確度,但定位器K在空間擴展的標準差1 mm以下才會有正確的定位估計,而且,從實驗可以看出定位器NAI的偽像範圍較小,且比較能壓低其他地區的活化程度估值。
This thesis focus on the problem in source imaging of auditory steady-state responses in MEG signals. When an audio stimulus is simultaneously presented to the ears of a subject, the brain waves recorded from the subject often have fully correlated sources. Conventional spatial filters cannot accurately estimate correlated sources because it’s assumed that all sources are not cross-correlated. The method in this thesis is dual-core beamformer (DCBF). There are some papers discussing the limitation of DCBF, but the effect of spatial extent on the performance of DCBF remains unknown. The effects of noise types of background sources, and of spatial extents of correlated sources on DCBF localizers are investigated in this thesis. In results, localizer-NAI is better than localizer-K. When the standard deviations of spatial extents of correlated sources are less than 5 mm, localizer-NAI is not affected. But localizer-K only works well when the standard deviations of spatial extents of correlated sources are less than 1 mm. Furthermore, localizer-NAI has much smaller range of artifacts, which means localizer-NAI can suppress the estimation of other source locations than localizer-K.
中文摘要 i
英文摘要 ii
誌謝 ii
目錄 iii
表目錄 iv
圖目錄 v
一、緒論 1
1.1 研究動機 1
1.2 腦磁圖設備介紹與製造廠簡介 3
1.3 MEG的訊號特性 5
1.4 聽覺神經科學簡介 6
1.4.1 聽覺穩態響應 6
二、 神經產生電磁場原理與正向模型 7
2.1 活動源模型──電流偶極子 9
2.2 正向模型的磁場積分 11
2.3 球狀顱部模型 13
2.3.1 MEG在球狀顱部正向模型的限制 15
2.4 正向模型對逆問題的影響與其對實驗設計的啟發 16
2.5 分佈式偶極子活動源模型 18
三、 逆問題與腦波訊號的波束成形器 20
3.1 逆問題與活動源模型的數學關係 20
3.1.1 正向模型對MEG或EEG活動源重建的影響與比較 21
3.2 波束成形器 22
3.2.1 線性約束最小變異波束成形器 23
3.3 完全相關活動源的重建問題 25
四、 可重建完全相關活動源的波束成形器 27
4.1 論文回顧 27
4.2 雙核波束成形器 28
4.2.1 雙核波束成形器的兩種定位器 28
4.2.2 雙核波束成形器的限制 29
五、 電腦模擬與雙核波束成形器的計算結果 30
5.1 電腦模擬 30
5.1.1 模擬1 完全相關的活動源但沒有在空間上擴展 31
5.1.2 模擬2 空間擴展的且完全相關活動源 32
5.1.3 模擬3 比較不同空間擴展範圍且完全相關的活動源 32
5.2 雙核波束成形器的計算結果 34
5.2.1 雙核波束成形器對模擬1的計算結果 35
5.2.2 雙核波束成形器對模擬2的計算結果 36
5.2.3 雙核波束成形器對模擬3的計算結果 37
六、 結論 39
七、 未來展望 40
參考文獻 41
[1] M.Tervaniemi andK.Hugdahl, “Lateralization of auditory-cortex functions,” Brain Res. Rev., vol. 43, no. 3, pp. 231–246, 2003.
[2] B. D.VanVeen, W.VanDrongelen, M.Yuchtman, andA.Suzuki, “Localization of brain electrical activity via linearly constrained minimum variance spatial filtering.,” IEEE Trans. Biomed. Eng., vol. 44, no. 9, pp. 867–880, 1997.
[3] J.Malmivuo, V.Suihko, andH.Eskola, “Sensitivity distributions of EEG and MEG measurements,” IEEE Trans. Biomed. Eng., vol. 44, no. 3, pp. 196–208, 1997.
[4] S.Baillet, J. C.Mosher, andR. M.Leahy, “Electromagnetic brain mapping,” IEEE Signal Process. Mag., vol. 18, no. 6, pp. 14–30, 2001.
[5] J.Bohórquez andÖ.Özdamar, “Generation of the 40-Hz auditory steady-state response (ASSR) explained using convolution,” Clin. Neurophysiol., vol. 119, no. 11, pp. 2598–2607, 2008.
[6] G. P.Jacobson, D. L.McCaslin, B.Smith, K.Elisevich, andP.Mishler, “Test-retest stability and short-term habituation of the N1 and gamma band response.,” J. Am. Acad. Audiol., vol. 10, no. 4, pp. 211–8, 1999.
[7] B.Ross, A. T.Herdman, andC.Pantev, “Right hemispheric laterality of human 40 Hz auditory steady-state responses,” Cereb. Cortex, vol. 15, no. 12, pp. 2029–2039, 2005.
[8] “Forward Model and Inversion.” [Online]. Available: http://neuroimage.usc.edu/brainstorm/Tutorials/HeadModel. [Accessed: 09-Jul-2016].
[9] S.Sillekens, “Influence of Volume Conduction on Beamformer Source Analysis in the Human Brain,” Westfälische Wilhelms-Universität Münster, 2008.
[10] M. S.Hämäläinen, R.Hari, R. J.Ilmoniemi, J.Knuutila, andO.VLounasmaa, “Magnetoencephalography - theory, instrumentation, and applications to noninvasivee studies of the working human brain,” Reviews of modern physics, vol. 65, no. 2. pp. 413–505, 1993.
[11] M.Gatta et al., “Magnetoencephalography in the study of brain dynamics,” Funct. Neurol., vol. 29, no. 4, pp. 64–70, 2014.
[12] J.Clarke andA. I.Braginski, The SQUID Handbook: Vol 2 Applications of SQUIDs and SQUID Systems. 2006.
[13] J.Malmivuo andR.Plonsey, Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields. New York: Oxford University Press, 1995.
[14] M.-X.Huang, J. C.Mosher, andR. M.Leahy, “A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG.,” Phys. Med. Biol., vol. 44, no. 2, pp. 423–40, 1999.
[15] J.Sarvas, “Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem.,” Phys. Med. Biol., vol. 32, no. 1, pp. 11–22, 1987.
[16] M.Lalancette, M.Quraan, andD.Cheyne, “Evaluation of multiple-sphere head models for MEG source localization,” Phys. Med. Biol., vol. 56, no. SEPTEMBER, pp. 5621–5635, 2011.
[17] V.Murzin, A.Fuchs, andJ. A. S.Kelso, “Anatomically constrained minimum variance beamforming applied to EEG,” Exp. Brain Res., vol. 214, no. 4, pp. 515–528, 2011.
[18] Z. A.Acar andS.Makeig, “Effects of forward model errors on EEG source localization,” Brain Topogr., vol. 26, no. 3, pp. 378–396, 2013.
[19] C. H.Wolters, A.Anwander, X.Tricoche, D.Weinstein, M. A.Koch, andR. S.MacLeod, “Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling,” Neuroimage, vol. 30, no. 3, pp. 813–826, 2006.
[20] D.Güllmar, J.Haueisen, andJ. R.Reichenbach, “Influence of anisotropic electrical conductivity in white matter tissue on the EEG/MEG forward and inverse solution. A high-resolution whole head simulation study,” Neuroimage, vol. 51, no. 1, pp. 145–163, 2010.
[21] G.Marin, C.Guerin, S.Baillet, L.Garnero, andG.Meunier, “Influence of skull anisotropy for the forward and inverse problem in EEG: Simulation studies using FEM on realistic head models,” Hum. Brain Mapp., vol. 6, no. 4, pp. 250–269, 1998.
[22] G.Nolte, “The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors,” Phys. Med. Biol., vol. 48, no. 22, pp. 1–16, 2003.
[23] M.Stenroos andJ.Sarvas, “Bioelectromagnetic forward problem: isolated source approach revis(it)ed,” Phys. Med. Biol., vol. 57, no. 11, pp. 3517–3535, 2012.
[24] M.Stenroos, A.Hunold, andJ.Haueisen, “Comparison of three-shell and simplified volume conductor models in magnetoencephalography,” Neuroimage, vol. 94, pp. 337–348, 2014.
[25] M. X.Cohen, Analyzing Neural Time Series Data: Theory and Practice. 2014.
[26] R. N.Henson, E.Mouchlianitis, andK. J.Friston, “MEG and EEG data fusion : Simultaneous localisation of face-evoked responses,” Neuroimage, vol. 47, no. 2, pp. 581–589, 2009.
[27] R. A.Chowdhury et al., “MEG–EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy,” Brain Topogr., vol. 28, no. 6, pp. 785–812, 2015.
[28] A. A.Ioannides, J. P. R.Bolton, andJ. J. S.Clarke, “Continuous probabilistic solutions to the biomagnetic inverse problem,” Physics (College. Park. Md)., vol. 6, pp. 523–542, 1990.
[29] K.Sekihara andS. S.Nagarajan, “Chapter 2 Sensor array outputs and spatial filters,” in Adaptive Spatial Filters for Electromagnetic Brain Imaging, 2008.
[30] R. J.Ilmoniemi, “Models of source currents in the brain,” Brain Topogr., vol. 5, no. 4, pp. 331–336, 1993.
[31] J. Z.Wang, S. J.Williamson, andL.Kaufman, “Magnetic Source Images Determined by a Lead-Field Analysis: The Unique Minimum-Norm Least-Squares Estimation,” IEEE Trans. Biomed. Eng., vol. 39, no. 7, pp. 665–675, 1992.
[32] R. D.Pascual-Marqui, C. M.Michel, andD.Lehmann, “Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain,” Int. J. Psychophysiol., vol. 18, no. 1, pp. 49–65, 1994.
[33] A.Moiseev, S. M.Doesburg, R. E.Grunau, andU.Ribary, “Minimum variance beamformer weights revisited,” Neuroimage, vol. 120, pp. 201–213, 2015.
[34] T.Halder, S.Talwar, A. K.Jaiswal, andA.Banerjee, “Performance evaluation of inverse methods for identification and characterization of oscillatory brain sources: Ground truth validation & empirical evidences,” bioRxiv, p. 395780, 2018.
[35] F. H.Lin, T.Witzel, T. A.Zeffiro, andJ. W.Belliveau, “Linear constraint minimum variance beamformer functional magnetic resonance inverse imaging,” Neuroimage, vol. 43, no. 2, pp. 297–311, 2008.
[36] W.VanDrongelen, M.Yuchtman, B. D.VanVeen, and a. C.VanHuffelen, “A spatial filtering technique to detect and localize multiple sources in the brain,” Brain Topogr., vol. 9, no. 1, pp. 39–49, 1996.
[37] A.Hillebrand, K. D.Singh, I. E.Holliday, P. L.Furlong, andG. R.Barnes, “A new approach to neuroimaging with magnetoencephalography,” Hum. Brain Mapp., vol. 25, no. 2, pp. 199–211, 2005.
[38] A.Fuchs, “Beamforming and its applications to brain connectivity,” Underst. Complex Syst., vol. 2007, pp. 357–378, 2007.
[39] J.Vrba andS. E.Robinson, “Signal processing in magnetoencephalography.,” Methods, vol. 25, no. 2, pp. 249–71, 2001.
[40] K.Sekihara, S. S.Nagarajan, D.Poeppel, andA.Marantz, “Asymptotic SNR of scalar and vector minimum-variance beanformers for neuromagnetic source reconstruction,” IEEE Trans. Biomed. Eng., vol. 51, no. 10, pp. 1726–1734, 2004.
[41] M. J.Brookes et al., “Beamformer reconstruction of correlated sources using a modified source model,” Neuroimage, vol. 34, no. 4, pp. 1454–1465, 2007.
[42] A. T.Herdman andD.Cheyne, “A Practical Guide for MEG and Beamforming,” in Brain Signal Analysis: Advances in Neuroelectric and Neuromagnetic Methods, no. February 2016, 2009, pp. 1–36.
[43] S. S.Dalal, K.Sekihara, andS. S.Nagarajan, “Modified beamformers for coherent source region suppression,” IEEE Trans. Biomed. Eng., vol. 53, no. 7, pp. 1357–1363, 2006.
[44] M.-X.Huang et al., “Commonalities and Differences among Vectorized Beamformers in Electromagnetic Source Imaging,” Brain Topogr., vol. 16, no. 3, pp. 139–158, 2004.
[45] M. A.Quraan andD.Cheyne, “Reconstruction of correlated brain activity with adaptive spatial filters in MEG,” Neuroimage, vol. 49, no. 3, pp. 2387–2400, 2010.
[46] M.Diwakar et al., “Dual-Core Beamformer for obtaining highly correlated neuronal networks in MEG,” Neuroimage, vol. 54, no. 1, pp. 253–263, 2010.
[47] M.Diwakar et al., “Accurate reconstruction of temporal correlation for neuronal sources using the enhanced dual-core MEG beamformer,” Neuroimage, vol. 56, no. 4, pp. 1918–1928, 2011.
[48] A.Moiseev andA. T.Herdman, “Multi-core beamformers: derivation, limitations and improvements.,” Neuroimage, vol. 71, pp. 135–46, 2013.
[49] S.Haufe andA.Ewald, “A Simulation Framework for Benchmarking EEG-Based Brain Connectivity Estimation Methodologies,” Brain Topogr., 2016.
[50] J. M.Gomes et al., “Intracellular Impedance Measurements Reveal Non-ohmic Properties of the Extracellular Medium around Neurons,” Biophys. J., vol. 110, no. 1, pp. 234–246, 2016.
[51] W. E.Kincses, C.Braun, S.Kaiser, andT.Elbert, “Modeling extended sources of event-related potentials using anatomical and physiological constraints,” Hum. Brain Mapp., vol. 8, no. 4, pp. 182–193, 1999.
[52] T.Limpiti, S.Member, B. D.VanVeen, andR. T.Wakai, “Cortical Patch Basis Model for Spatially Extended Neural Activity,” IEEE Trans. Biomed. Eng., vol. 53, no. 9, pp. 1740–1754, 2006.
[53] B.Cottereau, K.Jerbi, andS.Baillet, “Multiresolution imaging of MEG cortical sources using an explicit piecewise model,” Neuroimage, vol. 38, no. 3, pp. 439–451, 2007.
[54] L.Ding andB.He, “Sparse source imaging in electroencephalography with accurate field modeling,” Hum. Brain Mapp., vol. 29, no. 9, pp. 1053–1067, 2008.
[55] J. D.López, V.Litvak, J. J.Espinosa, K. J.Friston, andG. R.Barnes, “Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM,” Neuroimage, vol. 84, pp. 476–487, 2014.
[56] A.Sohrabpour, Y.Lu, G.Worrell, andB.He, “Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy,” Neuroimage, vol. 142, pp. 27–42, 2016.
[57] H.Becker et al., “EEG extended source localization: Tensor-based vs. conventional methods,” Neuroimage, vol. 96, pp. 143–157, 2014.
[58] F.Cong, Q.-H.Lin, L.-D.Kuang, X.-F.Gong, P.Astikainen, andT.Ristaniemi, “Tensor decomposition of EEG signals: A brief review,” J. Neurosci. Methods, vol. 248, pp. 59–69, 2015.
[59] H.Becker, L.Albera, andP.Comon, “Brain source imaging : from sparse to tensor models,” vol. c, no. 2, 2015.
[60] E.Karahan, P. A.Rojas-lopez, M. L.Bringas-vega, P. A.Valdés-Hernández, andP. A.Valdes-Sosa, “Tensor Analysis and Fusion of Multimodal Brain Images,” Proc. IEEE, vol. 103, no. 9, pp. 1531–1559, 2015.
[61] J.Dauwels, K.Srinivasan, andR. R.M, “Multi-channel EEG compression based on matrix and tensor decompositions,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., pp. 637–640, 2011.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top