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
  • [31] A NOVEL APPROACH FOR ENVIRONMENTAL MONITORING BASED ON THE INTEGRATION OF MULTI-TEMPORAL MULTI-SOURCE EARTH OBSERVATION DATA AND FIELD SURVEYS IN A SPATIO-TEMPORAL FRAMEWORK
    Paris, Claudia
    Kotowska, Martyna M.
    Erasmi, Stefan
    Schlund, Michael
    2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 5897 - 5900
  • [32] Comprehensive vitality evaluation and spatio-temporal characteristics of nighttime tourism in Shenzhen: an empirical analysis based on multi-source data integration
    Xu, Heng
    Zhang, Youyin
    Liu, Wenting
    Shen, Yunqing
    CURRENT ISSUES IN TOURISM, 2024,
  • [33] Mapping spatio-temporal patterns and detecting the factors of traffic congestion with multi-source data fusion and mining techniques
    Song, Jinchao
    Zhao, Chunli
    Zhong, Shaopeng
    Nielsen, Thomas Alexander Sick
    Prishchepov, Alexander V.
    COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, 2019, 77
  • [34] Improving spatio-temporal precipitation estimates in data scarce river basins: an application of machine learning-based multi-source data merging
    Mohammed, Juhar
    Mengiste, Yenesew
    Singh, Vijay P.
    STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2023, 37 (04) : 1353 - 1369
  • [35] Improving spatio-temporal precipitation estimates in data scarce river basins: an application of machine learning-based multi-source data merging
    Juhar Mohammed
    Yenesew Mengiste
    Vijay P. Singh
    Stochastic Environmental Research and Risk Assessment, 2023, 37 : 1353 - 1369
  • [36] Spatio-temporal modeling of neural source activation from EEG data
    Albu, Alexandra Branzan
    Mahajan, Sunny Vardhan
    Zeman, Philip M.
    Tanaka, James W.
    2007 CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, VOLS 1-3, 2007, : 1014 - 1017
  • [37] MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction
    Fang, Shen
    Prinet, Veronique
    Chang, Jianlong
    Werman, Michael
    Zhang, Chunxia
    Xiang, Shiming
    Pan, Chunhong
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (07) : 7142 - 7155
  • [38] Multi-source domain adaptation with spatio-temporal feature extractor for EEG emotion recognition
    Guo, Wenhui
    Xu, Guixun
    Wang, Yanjiang
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2023, 84
  • [39] Monitoring spatio-temporal dynamics of multi-dimensional karst ecosystem quality in Southwest China by integrating multi-source data
    Shen, Weiling
    Sun, Yuan
    Li, Jiaguo
    Zhang, Hongsheng
    Ding, Zhi
    Tang, Xuguang
    INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2024, 17 (01)
  • [40] Multi-Source Based Spatio-Temporal Distribution of Snow in a Semi-Arid Headwater Catchment of Northern Mongolia
    Munkhjargal, Munkhdavaa
    Groos, Simon
    Pan, Caleb G.
    Yadamsuren, Gansukh
    Yamkin, Jambaljav
    Menzel, Lucas
    GEOSCIENCES, 2019, 9 (01)