Spatio-temporal modeling of neuromagnetic data .2. Multi-source resolvability of a MUSIC-based location estimator

被引:0
|
作者
Supek, S
Aine, CJ
机构
[1] LOS ALAMOS NATL LAB, BIOPHYS GRP, LOS ALAMOS, NM 87545 USA
[2] UNIV ZAGREB, FAC SCI, DEPT PHYS, ZAGREB 10000, CROATIA
关键词
magnetoencephalography; models; statistical; theoretical; numerical analysis; computer-assisted; data interpretation; signal processing; computer assisted;
D O I
暂无
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
A MUltiple SIgnal Classification-based (MUSIC) approach for neuromagnetic multi-source localization (Mosher ct al. [1992] (IEEE Trans Med Eng BME-39:541-557) was evaluated through numerical simulations and by applying it to visually evoked neuromagnetic responses. A series of two-dipole and three-dipole spatio-temporal data sz ere generated to examine effects of 1) source configurations, 2) temporal correlations, 3) noise, and 4) subspace dimensionality assumptions on the number of MUSIC metric maxima, their amplitudes, and how the resulting metric maxima locations relate to the actual source locations. Ln its present form, i.e., using simple one-dipole scanning over an assumed source subspace, MUSIC resulted either in 1) peaks sufficiently close to 1, but fewer than the actual number of sources which affected location estimation accuracy, or 2) the peaks were too low to qualify as source locations. Our simulations indicate difficulties in defining threshold values as to which peak values are close enough to 1 while avoiding significant type IS errors (i.e., accepting peaks which should not be interpreted as source locations). Modifications to the MUSIC approach are necessary in order for the method to be considered of practical value for reliably localizing multiple neuromagnetic sources in empirical cases in which a high degree of temporal correlation between sources is likely (e.g., visual data). (C) 1997 Wiley-Liss, Inc.
引用
收藏
页码:154 / 167
页数:14
相关论文
共 50 条
  • [21] An Improved Multi-Source Data-Driven Landslide Prediction Method Based on Spatio-Temporal Knowledge Graph
    Chen, Luanjie
    Ge, Xingtong
    Yang, Lina
    Li, Weichao
    Peng, Ling
    REMOTE SENSING, 2023, 15 (08)
  • [22] Multi-source and heterogeneous marine hydrometeorology spatio-temporal data analysis with machine learning: a survey
    Wu, Song
    Li, Xiaoyong
    Dong, Wei
    Wang, Senzhang
    Zhang, Xiaojiang
    Xu, Zichen
    WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, 2023, 26 (03): : 1115 - 1156
  • [23] Spatio-temporal deep learning model for accurate streamflow prediction with multi-source data fusion
    Wang, Zhaocai
    Xu, Nannan
    Bao, Xiaoguang
    Wu, Junhao
    Cui, Xuefei
    ENVIRONMENTAL MODELLING & SOFTWARE, 2024, 178
  • [24] A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data
    Zhu, Lilu
    Su, Xiaolu
    Hu, Yanfeng
    Tai, Xianqing
    Fu, Kun
    REMOTE SENSING, 2021, 13 (12)
  • [25] MDTP: A Multi-source Deep Traffic Prediction Framework over Spatio-Temporal Trajectory Data
    Fang, Ziquan
    Pan, Lu
    Chen, Lu
    Du, Yuntao
    Gao, Yunjun
    PROCEEDINGS OF THE VLDB ENDOWMENT, 2021, 14 (08): : 1289 - 1297
  • [26] Multi-source and heterogeneous marine hydrometeorology spatio-temporal data analysis with machine learning: a survey
    Song Wu
    Xiaoyong Li
    Wei Dong
    Senzhang Wang
    Xiaojiang Zhang
    Zichen Xu
    World Wide Web, 2023, 26 : 1115 - 1156
  • [27] Spatio-temporal pattern evolution and regulatory zoning of suitability for farmland scale utilization in China based on multi-source data
    Tang, Feng
    Wang, Li
    Fu, Meichen
    Huang, Ni
    Li, Wang
    Song, Wanjuan
    Nath, Biswajit
    Ding, Shengping
    Niu, Zheng
    ECOLOGICAL INDICATORS, 2024, 166
  • [28] Resolvability of MUSIC algorithm in solving multiple dipole biomagnetic localization from spatio-temporal MCG data
    Chen, JG
    Niki, N
    Nakaya, Y
    Nishitani, H
    Kang, YM
    MEDICAL IMAGING 1998: IMAGE PROCESSING, PTS 1 AND 2, 1998, 3338 : 457 - 465
  • [29] Spatio-temporal characteristics of surface evapotranspiration in source region of rivers in Southwest China based on multi-source products
    Wen X.
    Zhou J.
    Liu S.
    Ma Y.
    Xu Z.
    Ma J.
    Water Resources Protection, 2021, 37 (03) : 32 - 42
  • [30] Spatio-Temporal Event Forecasting Using Incremental Multi-Source Feature Learning
    Zhao, Liang
    Gao, Yuyang
    Ye, Jieping
    Chen, Feng
    Ye, Yanfang
    Lu, Chang-Tien
    Ramakrishnan, Naren
    ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 2022, 16 (02)