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eeg system curry 7  (Compumedics)

 
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    Compumedics eeg system curry 7
    Eeg System Curry 7, supplied by Compumedics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/eeg system curry 7/product/Compumedics
    Average 90 stars, based on 1 article reviews
    eeg system curry 7 - by Bioz Stars, 2026-03
    90/100 stars

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    The framework of discharge‐affecting network analysis using emiCCA. A: For fMRI data, group ICA was first applied to extract the spatiotemporal features of the fMRI data; then, the IC time courses were concatenated across JME patients and defined as dataset Y. For <t>EEG</t> <t>data,</t> the onsets of GSWDs were first identified by neurologists. Then, the dataset X was defined by a design matrix containing the onsets of GSWDs, which were convolved with 4 SPM canonical HRFs (peaking at 3, 5, 7 and 9 s), 1 Glover HRF and 1 single Gamma HRF. B: emiCCA was applied to identify the linear and nonlinear discharge‐affecting components with weights (α) exceeding the 1.5 standard deviations of weight values corresponding to the significant maximal information eigen coefficients (MIECs). C: The maximal time‐lagged correlation method was used to examine the possible functional network connectivity between those discharge‐affecting networks identified by emiCCA. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]
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    The framework of discharge‐affecting network analysis using emiCCA. A: For fMRI data, group ICA was first applied to extract the spatiotemporal features of the fMRI data; then, the IC time courses were concatenated across JME patients and defined as dataset Y. For EEG data, the onsets of GSWDs were first identified by neurologists. Then, the dataset X was defined by a design matrix containing the onsets of GSWDs, which were convolved with 4 SPM canonical HRFs (peaking at 3, 5, 7 and 9 s), 1 Glover HRF and 1 single Gamma HRF. B: emiCCA was applied to identify the linear and nonlinear discharge‐affecting components with weights (α) exceeding the 1.5 standard deviations of weight values corresponding to the significant maximal information eigen coefficients (MIECs). C: The maximal time‐lagged correlation method was used to examine the possible functional network connectivity between those discharge‐affecting networks identified by emiCCA. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

    Journal: Human Brain Mapping

    Article Title: Complex discharge‐affecting networks in juvenile myoclonic epilepsy: A simultaneous EEG‐fMRI study

    doi: 10.1002/hbm.23256

    Figure Lengend Snippet: The framework of discharge‐affecting network analysis using emiCCA. A: For fMRI data, group ICA was first applied to extract the spatiotemporal features of the fMRI data; then, the IC time courses were concatenated across JME patients and defined as dataset Y. For EEG data, the onsets of GSWDs were first identified by neurologists. Then, the dataset X was defined by a design matrix containing the onsets of GSWDs, which were convolved with 4 SPM canonical HRFs (peaking at 3, 5, 7 and 9 s), 1 Glover HRF and 1 single Gamma HRF. B: emiCCA was applied to identify the linear and nonlinear discharge‐affecting components with weights (α) exceeding the 1.5 standard deviations of weight values corresponding to the significant maximal information eigen coefficients (MIECs). C: The maximal time‐lagged correlation method was used to examine the possible functional network connectivity between those discharge‐affecting networks identified by emiCCA. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

    Article Snippet: All EEG data were primarily analyzed using Curry 7 (Neuroscan software).

    Techniques: Functional Assay