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principal component analysis (pca) statistics toolbox  (MathWorks Inc)


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    MathWorks Inc principal component analysis (pca) statistics toolbox
    Principal Component Analysis (Pca) Statistics Toolbox, 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/result/principal component analysis (pca) statistics toolbox/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    principal component analysis (pca) statistics toolbox - by Bioz Stars, 2026-05
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    principal component analysis (pca) statistics toolbox  (MathWorks Inc)
    90
    MathWorks Inc principal component analysis (pca) statistics toolbox
    Principal Component Analysis (Pca) Statistics Toolbox, 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
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    Average 90 stars, based on 1 article reviews
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    principal component analysis pca  (MathWorks Inc)
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    MathWorks Inc principal component analysis pca
    Separation of global and local hemodynamic changes using <t>PCA.</t> ( a ) The averaged HbO concentration change among six animals recovered from the maximum peak of PC1 and PC2. The asterisk (*) indicates the statistically significant difference (repeated measures one-way ANOVA with Tukey-Kramer post-hoc analysis; p < 0.05) in amplitude varying by tUS parameter. Error bars indicate the standard deviation between subjects. ( b ) The average of the normalized two components of the HbO signal for three different paradigms in a representative mouse. Error bars indicate the standard deviation between paradigms. The second <t>principal</t> <t>component</t> correlates strongly with the associated HRF for the stimulation. ( c ) The corresponding images from the first (top row) and second (bottom row) principal components for each paradigm. The color bar indicates the normalized magnitude of PC coefficients. The white dots indicate the bregma.
    Principal Component Analysis Pca, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    principal components analysis (pca) of the statistics toolbox of matlab 7.0  (MathWorks Inc)
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    Separation of global and local hemodynamic changes using <t>PCA.</t> ( a ) The averaged HbO concentration change among six animals recovered from the maximum peak of PC1 and PC2. The asterisk (*) indicates the statistically significant difference (repeated measures one-way ANOVA with Tukey-Kramer post-hoc analysis; p < 0.05) in amplitude varying by tUS parameter. Error bars indicate the standard deviation between subjects. ( b ) The average of the normalized two components of the HbO signal for three different paradigms in a representative mouse. Error bars indicate the standard deviation between paradigms. The second <t>principal</t> <t>component</t> correlates strongly with the associated HRF for the stimulation. ( c ) The corresponding images from the first (top row) and second (bottom row) principal components for each paradigm. The color bar indicates the normalized magnitude of PC coefficients. The white dots indicate the bregma.
    Principal Components Analysis (Pca) Of The Statistics Toolbox Of Matlab 7.0, 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/result/principal components analysis (pca) of the statistics toolbox of matlab 7.0/product/MathWorks Inc
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    Separation of global and local hemodynamic changes using PCA. ( a ) The averaged HbO concentration change among six animals recovered from the maximum peak of PC1 and PC2. The asterisk (*) indicates the statistically significant difference (repeated measures one-way ANOVA with Tukey-Kramer post-hoc analysis; p < 0.05) in amplitude varying by tUS parameter. Error bars indicate the standard deviation between subjects. ( b ) The average of the normalized two components of the HbO signal for three different paradigms in a representative mouse. Error bars indicate the standard deviation between paradigms. The second principal component correlates strongly with the associated HRF for the stimulation. ( c ) The corresponding images from the first (top row) and second (bottom row) principal components for each paradigm. The color bar indicates the normalized magnitude of PC coefficients. The white dots indicate the bregma.

    Journal: Scientific Reports

    Article Title: Monitoring cerebral hemodynamic change during transcranial ultrasound stimulation using optical intrinsic signal imaging

    doi: 10.1038/s41598-017-13572-0

    Figure Lengend Snippet: Separation of global and local hemodynamic changes using PCA. ( a ) The averaged HbO concentration change among six animals recovered from the maximum peak of PC1 and PC2. The asterisk (*) indicates the statistically significant difference (repeated measures one-way ANOVA with Tukey-Kramer post-hoc analysis; p < 0.05) in amplitude varying by tUS parameter. Error bars indicate the standard deviation between subjects. ( b ) The average of the normalized two components of the HbO signal for three different paradigms in a representative mouse. Error bars indicate the standard deviation between paradigms. The second principal component correlates strongly with the associated HRF for the stimulation. ( c ) The corresponding images from the first (top row) and second (bottom row) principal components for each paradigm. The color bar indicates the normalized magnitude of PC coefficients. The white dots indicate the bregma.

    Article Snippet: In addition, principal component analysis (PCA) (Statistics and Machine Learning Toolbox, Matlab, Mathworks Inc., USA) was applied to remove motion artefacts and decompose the signal into different sources .

    Techniques: Concentration Assay, Standard Deviation