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| 研究生： |
黃時霖 Shih-Lin Huang |
|---|---|
| 論文名稱： |
基於最小範數法的腦電磁訊號源定位 Source localization of brain electromagnetic signals based on minimum norm method |
| 指導教授： |
王敏生
Min-Sheng Wang 黎璧賢 Pik-Yin Lai |
| 口試委員: | |
| 學位類別： |
博士 Doctor |
| 系所名稱： |
理學院 - 物理學系 Department of Physics |
| 論文出版年： | 2020 |
| 畢業學年度： | 108 |
| 語文別： | 中文 |
| 論文頁數： | 48 |
| 中文關鍵詞： | 最小範數估計 、腦磁圖儀 、腦電圖儀 、逆問題 、奇異值分解 、源分布迭代最小範數法 |
| 外文關鍵詞： | MNE, MEG, EEG, inverse problem, SVD, source iteration of minimum norm, SIMN |
| 相關次數： | 點閱：17 下載：0 |
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我們提出了一個在最小範數法的架構內，從腦磁/電圖估算腦可能的活動區域的方法。這個方法將整個大腦以格點覆蓋，每個格點和少量格點組合求最小範數解，利用少量格點的組合之中源的解會大於多數非源格點解的性質，經由統計每個格點在多次不同的組合中解大於每個組合半數格點之解的次數，篩選出可能是源的格點，再對篩選出的格點進行源分布迭代(SIMN)定位源的位置。數值模擬顯示本方法大幅提升源定位正確率，用人臉辨識腦磁圖數據對本方法進行測試，結果和fMRI的結果一致。
A method in the framework of minimum norm estimate(MNE) is proposed to identify the possible active regions of the brain from MEG/EEG data. The whole brain is covered with grid points. Each grid point is teamed up with a small number of grid points to calculate the MNE of these points. In a group of few grid points, the MNE of source grid point is in general greater than that of non-source grid points. By considering the ensemble of each grid point teaming up with few grid points of a fixed number, the statistic of the number of times that each grid point’s MNE is greater than half of the grid points’ MNE in each combination enables us to identify the possible source grid points. SIMN is applied to these grid points to determine the positions of sources. Numerical simulations show that the accuracy of source localization is greatly improved. This method is applied to the facial recognition MEG data and the results are consistent with the results of fMRI.
Allison, T., Puce, A., & McCarthy, G. (2000). Social perception from visual cues: role of the STS region. Trends in cognitive sciences, 4(7), 267–278.
Beason-Held, L. L., Purpura, K. P., Krasuski, J. S., Maisog, J. M., Daly, E. M., Mangot, D. J., Desmond, R. E., Optican, L. M., Schapiro, M. B., & VanMeter, J. W. (1998). Cortical regions involved in visual texture perception: a fMRI study. Brain research. Cognitive brain research, 7(2), 111–118.
Bentin, S., Allison, T., Puce, A., Perez, E., & McCarthy, G. (1996). Electrophysiological Studies of Face Perception in Humans. Journal of cognitive neuroscience, 8(6), 551–565.
Dale, A. M., Liu, A. K., Fischl, B. R., Buckner, R. L., Belliveau, J. W., Lewine, J. D., & Halgren, E. (2000). Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron, 26(1), 55–67.
Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D. H., Busa, E., Seidman, L. J., Goldstein, J., Kennedy, D., Caviness, V., Makris, N., Rosen, B., & Dale, A. M. (2004). Automatically parcellating the human cerebral cortex. Cerebral cortex (New York, N.Y. : 1991), 14(1), 11–22.
Hämäläinen, M. S., & Ilmoniemi, R. J. (1994). Interpreting magnetic fields of the brain: minimum norm estimates. Medical & biological engineering & computing, 32(1), 35–42.
Hansen, P. C., & O’Leary, D. P. (1993). The use of the l-curve in the regularization of discrete ill-posed problems. Siam Journal on Scientific Computing, 14(6), 1487-1503.
Ioannides, A. A., Bolton, J. P. R., Clarke, C. J. S. (1990). Continuous probabilistic solutions to the biomagnetic inverse problem. Inverse Probl 6(4), 523-542.
Liang, W. K., & Wang, M. S. (2009). Source reconstruction of brain electromagnetic fields--source iteration of minimum norm (SIMN). NeuroImage, 47(4), 1301–1311.
Liu, A. K., Belliveau, J. W., & Dale, A. M. (1998). Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations. Proceedings of the National Academy of Sciences of the United States of America, 95(15), 8945–8950.
Pascual-Marqui R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods and findings in experimental and clinical pharmacology, 24 Suppl D, 5–12.
Sarvas J. (1987). Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Physics in medicine and biology, 32(1), 11–22.
Ungerleider, L. G., & Haxby, J. V. (1994). 'What' and 'where' in the human brain. Current opinion in neurobiology, 4(2), 157–165.
Van Veen, B. D., van Drongelen, W., Yuchtman, M., & Suzuki, A. (1997). Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE transactions on bio-medical engineering, 44(9), 867–880.
Wakeman, D. G., & Henson, R. N. (2015). A multi-subject, multi-modal human neuroimaging dataset. Scientific data, 2,
Yao, Z., & Eddy, W. (2014). A STATISTICAL APPROACH TO THE INVERSE PROBLEM IN MAGNETOENCEPHALOGRAPHY. The Annals of Applied Statistics, 8(2), 1119-1144.
