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standard nmf algorithm matlab 2016a  (MathWorks Inc)


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    MathWorks Inc standard nmf algorithm matlab 2016a
    Standard Nmf Algorithm Matlab 2016a, supplied by MathWorks Inc, 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/product/nmf+algorithm/pm31602691-479-14-13
    Average 90 stars, based on 1 article reviews
    standard nmf algorithm matlab 2016a - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    Extraction:

    Article Title: Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate.
    Article Snippet: .. The raw EEG data were preprocessed using MATLAB 2016a, including channel localization, removal of irrelevant electrodes, band-pass filtering (1–30 Hz), downsampling to 250 Hz, extraction of MI segments, Independent Component Analysis (ICA), removal of bad segments, and re-referencing to the average: (1) Channel localization: EEG data were matched to specific channel locations. (2) Removal electrodes: Non-analytical electrodes, including HEOL and HEOR, were removed. (3) Band-pass filtering: A 1–30 Hz band-pass filter was applied to retain primary EEG signals. (4) Down sampled: The sampling rate was reduced from 500 Hz to 250 Hz to improve computational efficiency. (5) Extraction epoch: EEG data corresponding to MI phases (marked as “2”) were extracted, resulting in 40 segments (Fig. 2b), including 20 left-hand MI segments (80 s) and 20 right-hand MI segments (80 s), for subsequent analysis. (6) Independent Component Analysis (ICA): ICA was applied to identify and remove artifacts caused by eye, cardiac, and muscle activity. ..

    Article Title: Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate
    Article Snippet: .. The raw EEG data were preprocessed using MATLAB 2016a, including channel localization, removal of irrelevant electrodes, band-pass filtering (1–30 Hz), downsampling to 250 Hz, extraction of MI segments, Independent Component Analysis (ICA), removal of bad segments, and re-referencing to the average: Channel localization: EEG data were matched to specific channel locations. ..

    Sampling:

    Article Title: Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate.
    Article Snippet: .. The raw EEG data were preprocessed using MATLAB 2016a, including channel localization, removal of irrelevant electrodes, band-pass filtering (1–30 Hz), downsampling to 250 Hz, extraction of MI segments, Independent Component Analysis (ICA), removal of bad segments, and re-referencing to the average: (1) Channel localization: EEG data were matched to specific channel locations. (2) Removal electrodes: Non-analytical electrodes, including HEOL and HEOR, were removed. (3) Band-pass filtering: A 1–30 Hz band-pass filter was applied to retain primary EEG signals. (4) Down sampled: The sampling rate was reduced from 500 Hz to 250 Hz to improve computational efficiency. (5) Extraction epoch: EEG data corresponding to MI phases (marked as “2”) were extracted, resulting in 40 segments (Fig. 2b), including 20 left-hand MI segments (80 s) and 20 right-hand MI segments (80 s), for subsequent analysis. (6) Independent Component Analysis (ICA): ICA was applied to identify and remove artifacts caused by eye, cardiac, and muscle activity. ..

    Activity Assay:

    Article Title: Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate.
    Article Snippet: .. The raw EEG data were preprocessed using MATLAB 2016a, including channel localization, removal of irrelevant electrodes, band-pass filtering (1–30 Hz), downsampling to 250 Hz, extraction of MI segments, Independent Component Analysis (ICA), removal of bad segments, and re-referencing to the average: (1) Channel localization: EEG data were matched to specific channel locations. (2) Removal electrodes: Non-analytical electrodes, including HEOL and HEOR, were removed. (3) Band-pass filtering: A 1–30 Hz band-pass filter was applied to retain primary EEG signals. (4) Down sampled: The sampling rate was reduced from 500 Hz to 250 Hz to improve computational efficiency. (5) Extraction epoch: EEG data corresponding to MI phases (marked as “2”) were extracted, resulting in 40 segments (Fig. 2b), including 20 left-hand MI segments (80 s) and 20 right-hand MI segments (80 s), for subsequent analysis. (6) Independent Component Analysis (ICA): ICA was applied to identify and remove artifacts caused by eye, cardiac, and muscle activity. ..

    Software:

    Article Title: Herpes simplex virus type 1 reshapes host chromatin architecture via transcription machinery hijacking.
    Article Snippet: .. For ONI images, localization lists were obtained using the integrated simultaneous localization software from ONI (ONI NimOS v.10.5) and subsequently converted into Insight3 compatible files for post-processing using a custom-built software in MATLAB 2016a. ..

    Concentration Assay:

    Article Title: 3D printed vitamin D impregnated catheters for magnetic resonance-guided interventions: proof of concept and imaging characteristics.
    Article Snippet: .. Differences in mean SNR and CNR (averaged over all four flip angles) for isomer concentration were examined using scatter plots, box plots, and nonparametric comparisons using the Dunn method, with saline as the control group in MATLAB 2016a (MathWorks, Natick, MA). .. Additional nonparametric comparisons were performed using the Mann–Whitney test in Prism 10 (GraphPad Software, Boston, MA) to assess T1 values across vitamin D isomer solution concentrations (100%, n = 32 and 50%, n = 32) compared to saline, n = 36.

    Saline:

    Article Title: 3D printed vitamin D impregnated catheters for magnetic resonance-guided interventions: proof of concept and imaging characteristics.
    Article Snippet: .. Differences in mean SNR and CNR (averaged over all four flip angles) for isomer concentration were examined using scatter plots, box plots, and nonparametric comparisons using the Dunn method, with saline as the control group in MATLAB 2016a (MathWorks, Natick, MA). .. Additional nonparametric comparisons were performed using the Mann–Whitney test in Prism 10 (GraphPad Software, Boston, MA) to assess T1 values across vitamin D isomer solution concentrations (100%, n = 32 and 50%, n = 32) compared to saline, n = 36.

    Control:

    Article Title: 3D printed vitamin D impregnated catheters for magnetic resonance-guided interventions: proof of concept and imaging characteristics.
    Article Snippet: .. Differences in mean SNR and CNR (averaged over all four flip angles) for isomer concentration were examined using scatter plots, box plots, and nonparametric comparisons using the Dunn method, with saline as the control group in MATLAB 2016a (MathWorks, Natick, MA). .. Additional nonparametric comparisons were performed using the Mann–Whitney test in Prism 10 (GraphPad Software, Boston, MA) to assess T1 values across vitamin D isomer solution concentrations (100%, n = 32 and 50%, n = 32) compared to saline, n = 36.

    Derivative Assay:

    Article Title: 突发公共卫生事件健康相关信息质量监管的三方博弈及仿真分析
    Article Snippet: .. Replicator dynamic equations and Jacobian matrices were derived to analyze the stability of equilibrium points, while MATLAB 2016a simulations were conducted to validate the model under varying initial conditions ( e.g ., I yy =100, 150, 200; P yy =0, 20, 35; F yy =0, 10, 20). ..

    other:

    Article Title: Deep vision transformers in neurodegenerative disease diagnosis using 18F-fluorodeoxyglucose positron emission tomography scans and anatomical brain atlas
    Article Snippet: The MathWorks Inc. Matlab R; 2016a.



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    Image Search Results


    Non-negative Matrix Factorization of a typical record from a P1 animal. A Grayscale reference image followed by the 13 features required to capture 90% of the variance of the original data, displayed in descending order of relative prominence in sleep vs wake. Note the smaller total number of features required capture the same fraction of variance of the data compared to P6-P8 and the more even weighting of those features between sleep (8) and wake (5), reflecting the more evenly distributed complexity of activity between the two states at P1. Scale bar represents 1mm. B: Time vectors and records as in Fig. 5. C: Relationship between number of features (NMF Rank) and fraction of variance captures for P6-P8 vs P1 animals. On average, fewer features are required to capture equal variances at P1. D-F: Bivariate distributions of Feature Area vs. Sleep Score at P6-P8 (D) and P1 (E), and the Sleep-Wake difference plot (F). Development during the first postnatal week is characterized by added features with wider spread (20–40% area coverage) in sleep (Sleep Score 0.5–1.2). P-values determined using permutation test.

    Journal: Developmental neurobiology

    Article Title: Large-scale waves of activity in the neonatal mouse brain in vivo occur almost exclusively during sleep cycles

    doi: 10.1002/dneu.22901

    Figure Lengend Snippet: Non-negative Matrix Factorization of a typical record from a P1 animal. A Grayscale reference image followed by the 13 features required to capture 90% of the variance of the original data, displayed in descending order of relative prominence in sleep vs wake. Note the smaller total number of features required capture the same fraction of variance of the data compared to P6-P8 and the more even weighting of those features between sleep (8) and wake (5), reflecting the more evenly distributed complexity of activity between the two states at P1. Scale bar represents 1mm. B: Time vectors and records as in Fig. 5. C: Relationship between number of features (NMF Rank) and fraction of variance captures for P6-P8 vs P1 animals. On average, fewer features are required to capture equal variances at P1. D-F: Bivariate distributions of Feature Area vs. Sleep Score at P6-P8 (D) and P1 (E), and the Sleep-Wake difference plot (F). Development during the first postnatal week is characterized by added features with wider spread (20–40% area coverage) in sleep (Sleep Score 0.5–1.2). P-values determined using permutation test.

    Article Snippet: The resulting matrix was factored into a series of paired spatial and temporal components, using the MatLab implementation of the alternating least squares algorithm for NMF.

    Techniques: Activity Assay