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
  • [1] Spatio-temporal modeling of neuromagnetic data .1. Multi-source location versus time-course estimation accuracy
    Supek, S
    Aine, CJ
    HUMAN BRAIN MAPPING, 1997, 5 (03) : 139 - 153
  • [2] A Spatio-Temporal Prediction Method of Traffic Flow Based on Multi-Source Data
    Hu J.
    Gong Y.
    Cai S.
    Huang T.
    Qiche Gongcheng/Automotive Engineering, 2021, 43 (11): : 1662 - 1672
  • [3] Modeling Spatio-temporal Drought Events Based on Multi-temporal,Multi-source Remote Sensing Data Calibrated by Soil Humidity
    LI Hanyu
    KAUFMANN Hermann
    XU Guochang
    Chinese Geographical Science, 2022, 32 (01) : 127 - 141
  • [4] Modeling Spatio-temporal Drought Events Based on Multi-temporal,Multi-source Remote Sensing Data Calibrated by Soil Humidity
    LI Hanyu
    KAUFMANN Hermann
    XU Guochang
    Chinese Geographical Science, 2022, (01) : 127 - 141
  • [5] Modeling Spatio-temporal Drought Events Based on Multi-temporal, Multi-source Remote Sensing Data Calibrated by Soil Humidity
    Li Hanyu
    Kaufmann, Hermann
    Xu Guochang
    CHINESE GEOGRAPHICAL SCIENCE, 2022, 32 (01) : 127 - 141
  • [6] Prediction of Winter Wheat Yield Based on Fusing Multi-source Spatio-temporal Data
    Wang L.
    Zheng G.
    Guo Y.
    He J.
    Cheng Y.
    Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2022, 53 (01): : 198 - 204and458
  • [7] Multi-Source Spatio-Temporal Data Fusion Path Estimation Method
    Hu, Qinying
    Sun, Gege
    Chen, Hang
    ELECTRONICS, 2025, 14 (04):
  • [8] Modeling Spatio-temporal Drought Events Based on Multi-temporal, Multi-source Remote Sensing Data Calibrated by Soil Humidity
    Hanyu Li
    Hermann Kaufmann
    Guochang Xu
    Chinese Geographical Science, 2022, 32 : 127 - 141
  • [9] Spatio-Temporal Traffic Prediction of Wireless Communication Network Based on Multi-source Data
    Wang, Yu
    Sun, Yangyang
    Fan, Yanlin
    Jiang, Tao
    Xiong, Jiansheng
    Zhou, Ying
    Han, Zhibo
    IOT AS A SERVICE, IOTAAS 2023, 2025, 585 : 255 - 267
  • [10] WILDFIRE VULNERABILITY ASSESSMENT IN WESTERN SICHUAN CHINA BASED ON MULTI-SOURCE SPATIO-TEMPORAL DATA
    Zhao, Donglin
    2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 7930 - 7933