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fastica for matlab 2.5  (MathWorks Inc)


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    MathWorks Inc fastica for matlab 2.5
    (A) Time series (left), sensor topographies (middle), and localization results (right) of the saccade component of a single subject for second-order blind identification (SOBI) and adaptive mixture ICA (AMICA). The dots in the sensor topography plots indicate the root mean squares of the mixing matrix weights of the two orthogonal gradiometers at a given location. The localization plots display sagittal and coronal slices at the level of the eye. The pink triangles indicate the locations that are the most probable source areas of the illustrated component. (B) Time series, sensor topographies and localization results of the blink component of a single subject for <t>FastICA</t> and AMICA. (C) Time series, sensor topographies and localization results of the primary and secondary blink component of a single subject extracted with FastICA.
    Fastica For Matlab 2.5, 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/fastica for matlab 2.5/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    fastica for matlab 2.5 - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Removing ocular artifacts from magnetoencephalographic data on naturalistic reading of continuous texts"

    Article Title: Removing ocular artifacts from magnetoencephalographic data on naturalistic reading of continuous texts

    Journal: Frontiers in Neuroscience

    doi: 10.3389/fnins.2022.974162

    (A) Time series (left), sensor topographies (middle), and localization results (right) of the saccade component of a single subject for second-order blind identification (SOBI) and adaptive mixture ICA (AMICA). The dots in the sensor topography plots indicate the root mean squares of the mixing matrix weights of the two orthogonal gradiometers at a given location. The localization plots display sagittal and coronal slices at the level of the eye. The pink triangles indicate the locations that are the most probable source areas of the illustrated component. (B) Time series, sensor topographies and localization results of the blink component of a single subject for FastICA and AMICA. (C) Time series, sensor topographies and localization results of the primary and secondary blink component of a single subject extracted with FastICA.
    Figure Legend Snippet: (A) Time series (left), sensor topographies (middle), and localization results (right) of the saccade component of a single subject for second-order blind identification (SOBI) and adaptive mixture ICA (AMICA). The dots in the sensor topography plots indicate the root mean squares of the mixing matrix weights of the two orthogonal gradiometers at a given location. The localization plots display sagittal and coronal slices at the level of the eye. The pink triangles indicate the locations that are the most probable source areas of the illustrated component. (B) Time series, sensor topographies and localization results of the blink component of a single subject for FastICA and AMICA. (C) Time series, sensor topographies and localization results of the primary and secondary blink component of a single subject extracted with FastICA.

    Techniques Used:

    Number of components required by SOBI-FastICA and AMICA to extract saccade and blink artifacts and the Pearson correlations between the components extracted by the different methods, per artifact type.
    Figure Legend Snippet: Number of components required by SOBI-FastICA and AMICA to extract saccade and blink artifacts and the Pearson correlations between the components extracted by the different methods, per artifact type.

    Techniques Used:

    Reduction of signal power by SOBI-FastICA (Left) and AMICA (Right) , relative to the original uncleaned data, in six frequency bands (rows), averaged across subjects. More positive values indicate a larger reduction. In blue areas, the amount of reduced power was minimal and the DICS method erroneously estimated an increase in power (a negative reduction).
    Figure Legend Snippet: Reduction of signal power by SOBI-FastICA (Left) and AMICA (Right) , relative to the original uncleaned data, in six frequency bands (rows), averaged across subjects. More positive values indicate a larger reduction. In blue areas, the amount of reduced power was minimal and the DICS method erroneously estimated an increase in power (a negative reduction).

    Techniques Used:

    Group-level difference between power reduction by SOBI-FastICA and AMICA, relative to the power level of the AMICA pipeline. Red indicates areas where the SOBI-FastICA pipeline resulted in a larger reduction of signal power, whereas blue denotes areas where AMICA resulted in a larger reduction of signal power.
    Figure Legend Snippet: Group-level difference between power reduction by SOBI-FastICA and AMICA, relative to the power level of the AMICA pipeline. Red indicates areas where the SOBI-FastICA pipeline resulted in a larger reduction of signal power, whereas blue denotes areas where AMICA resulted in a larger reduction of signal power.

    Techniques Used:

    Difference between power reduction by SOBI-FastICA and AMICA in four individual subjects (no. 5, 7, 8, and 10; cf. ) for frequency bands 1–4, 15–25, and 60–90 Hz, relative to the power level of the AMICA pipeline. Red indicates areas where the SOBI-FastICA pipeline resulted in a larger reduction of signal power, whereas blue denotes areas where AMICA resulted in a larger reduction of signal power.
    Figure Legend Snippet: Difference between power reduction by SOBI-FastICA and AMICA in four individual subjects (no. 5, 7, 8, and 10; cf. ) for frequency bands 1–4, 15–25, and 60–90 Hz, relative to the power level of the AMICA pipeline. Red indicates areas where the SOBI-FastICA pipeline resulted in a larger reduction of signal power, whereas blue denotes areas where AMICA resulted in a larger reduction of signal power.

    Techniques Used:

    Individual variation of power reduction by SOBI-FastICA vs. AMICA in the six parcel and frequency band combinations for which the group-level difference between the methods exceeded 0.05. All such parcels were found in the right hemisphere. The results have been normalized by dividing them with the amount of power reduced by AMICA. Positive sign indicates that SOBI-FastICA reduced more power and negative sign that AMICA did. The colors denote the different parcel–frequency combinations. The colors were created with the linspacer function written by which uses colors derived mainly from ColorBrewer 2.0 .
    Figure Legend Snippet: Individual variation of power reduction by SOBI-FastICA vs. AMICA in the six parcel and frequency band combinations for which the group-level difference between the methods exceeded 0.05. All such parcels were found in the right hemisphere. The results have been normalized by dividing them with the amount of power reduced by AMICA. Positive sign indicates that SOBI-FastICA reduced more power and negative sign that AMICA did. The colors denote the different parcel–frequency combinations. The colors were created with the linspacer function written by which uses colors derived mainly from ColorBrewer 2.0 .

    Techniques Used: Derivative Assay

    Systematic group-level differences (two-sided t -test, p ≤ 0.005, uncorrected) in power reduction by SOBI-FastICA and AMICA. P -values illustrated. The “(no significant results)” texts indicate that no significant results were found on the given hemisphere on the given frequency band. Orange indicates areas where the SOBI-FastICA pipeline reduced more power, and blue areas where AMICA reduced more power.
    Figure Legend Snippet: Systematic group-level differences (two-sided t -test, p ≤ 0.005, uncorrected) in power reduction by SOBI-FastICA and AMICA. P -values illustrated. The “(no significant results)” texts indicate that no significant results were found on the given hemisphere on the given frequency band. Orange indicates areas where the SOBI-FastICA pipeline reduced more power, and blue areas where AMICA reduced more power.

    Techniques Used:

    Group-level mean differences in power reduction by SOBI-FastICA and AMICA for parcel-frequency band combinations containing points with systematic group-level differences (cf. <xref ref-type= Figure 6 )." title="Group-level mean differences in power reduction by SOBI-FastICA and AMICA for parcel-frequency band combinations ..." property="contentUrl" width="100%" height="100%"/>
    Figure Legend Snippet: Group-level mean differences in power reduction by SOBI-FastICA and AMICA for parcel-frequency band combinations containing points with systematic group-level differences (cf. Figure 6 ).

    Techniques Used:



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    fastica for matlab 2.5  (MathWorks Inc)
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    MathWorks Inc fastica for matlab 2.5
    (A) Time series (left), sensor topographies (middle), and localization results (right) of the saccade component of a single subject for second-order blind identification (SOBI) and adaptive mixture ICA (AMICA). The dots in the sensor topography plots indicate the root mean squares of the mixing matrix weights of the two orthogonal gradiometers at a given location. The localization plots display sagittal and coronal slices at the level of the eye. The pink triangles indicate the locations that are the most probable source areas of the illustrated component. (B) Time series, sensor topographies and localization results of the blink component of a single subject for <t>FastICA</t> and AMICA. (C) Time series, sensor topographies and localization results of the primary and secondary blink component of a single subject extracted with FastICA.
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    fastica 2.5 matlab package  (MathWorks Inc)
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    (A) Time series (left), sensor topographies (middle), and localization results (right) of the saccade component of a single subject for second-order blind identification (SOBI) and adaptive mixture ICA (AMICA). The dots in the sensor topography plots indicate the root mean squares of the mixing matrix weights of the two orthogonal gradiometers at a given location. The localization plots display sagittal and coronal slices at the level of the eye. The pink triangles indicate the locations that are the most probable source areas of the illustrated component. (B) Time series, sensor topographies and localization results of the blink component of a single subject for <t>FastICA</t> and AMICA. (C) Time series, sensor topographies and localization results of the primary and secondary blink component of a single subject extracted with FastICA.
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    Image Search Results


    (A) Time series (left), sensor topographies (middle), and localization results (right) of the saccade component of a single subject for second-order blind identification (SOBI) and adaptive mixture ICA (AMICA). The dots in the sensor topography plots indicate the root mean squares of the mixing matrix weights of the two orthogonal gradiometers at a given location. The localization plots display sagittal and coronal slices at the level of the eye. The pink triangles indicate the locations that are the most probable source areas of the illustrated component. (B) Time series, sensor topographies and localization results of the blink component of a single subject for FastICA and AMICA. (C) Time series, sensor topographies and localization results of the primary and secondary blink component of a single subject extracted with FastICA.

    Journal: Frontiers in Neuroscience

    Article Title: Removing ocular artifacts from magnetoencephalographic data on naturalistic reading of continuous texts

    doi: 10.3389/fnins.2022.974162

    Figure Lengend Snippet: (A) Time series (left), sensor topographies (middle), and localization results (right) of the saccade component of a single subject for second-order blind identification (SOBI) and adaptive mixture ICA (AMICA). The dots in the sensor topography plots indicate the root mean squares of the mixing matrix weights of the two orthogonal gradiometers at a given location. The localization plots display sagittal and coronal slices at the level of the eye. The pink triangles indicate the locations that are the most probable source areas of the illustrated component. (B) Time series, sensor topographies and localization results of the blink component of a single subject for FastICA and AMICA. (C) Time series, sensor topographies and localization results of the primary and secondary blink component of a single subject extracted with FastICA.

    Article Snippet: Therefore, the version of FastICA used in this study [FastICA for Matlab 2.5 ( )] and introduced by strives to obtain an independence-maximizing transformation by seeking one that minimizes the mutual information of the components.

    Techniques:

    Number of components required by SOBI-FastICA and AMICA to extract saccade and blink artifacts and the Pearson correlations between the components extracted by the different methods, per artifact type.

    Journal: Frontiers in Neuroscience

    Article Title: Removing ocular artifacts from magnetoencephalographic data on naturalistic reading of continuous texts

    doi: 10.3389/fnins.2022.974162

    Figure Lengend Snippet: Number of components required by SOBI-FastICA and AMICA to extract saccade and blink artifacts and the Pearson correlations between the components extracted by the different methods, per artifact type.

    Article Snippet: Therefore, the version of FastICA used in this study [FastICA for Matlab 2.5 ( )] and introduced by strives to obtain an independence-maximizing transformation by seeking one that minimizes the mutual information of the components.

    Techniques:

    Reduction of signal power by SOBI-FastICA (Left) and AMICA (Right) , relative to the original uncleaned data, in six frequency bands (rows), averaged across subjects. More positive values indicate a larger reduction. In blue areas, the amount of reduced power was minimal and the DICS method erroneously estimated an increase in power (a negative reduction).

    Journal: Frontiers in Neuroscience

    Article Title: Removing ocular artifacts from magnetoencephalographic data on naturalistic reading of continuous texts

    doi: 10.3389/fnins.2022.974162

    Figure Lengend Snippet: Reduction of signal power by SOBI-FastICA (Left) and AMICA (Right) , relative to the original uncleaned data, in six frequency bands (rows), averaged across subjects. More positive values indicate a larger reduction. In blue areas, the amount of reduced power was minimal and the DICS method erroneously estimated an increase in power (a negative reduction).

    Article Snippet: Therefore, the version of FastICA used in this study [FastICA for Matlab 2.5 ( )] and introduced by strives to obtain an independence-maximizing transformation by seeking one that minimizes the mutual information of the components.

    Techniques:

    Group-level difference between power reduction by SOBI-FastICA and AMICA, relative to the power level of the AMICA pipeline. Red indicates areas where the SOBI-FastICA pipeline resulted in a larger reduction of signal power, whereas blue denotes areas where AMICA resulted in a larger reduction of signal power.

    Journal: Frontiers in Neuroscience

    Article Title: Removing ocular artifacts from magnetoencephalographic data on naturalistic reading of continuous texts

    doi: 10.3389/fnins.2022.974162

    Figure Lengend Snippet: Group-level difference between power reduction by SOBI-FastICA and AMICA, relative to the power level of the AMICA pipeline. Red indicates areas where the SOBI-FastICA pipeline resulted in a larger reduction of signal power, whereas blue denotes areas where AMICA resulted in a larger reduction of signal power.

    Article Snippet: Therefore, the version of FastICA used in this study [FastICA for Matlab 2.5 ( )] and introduced by strives to obtain an independence-maximizing transformation by seeking one that minimizes the mutual information of the components.

    Techniques:

    Difference between power reduction by SOBI-FastICA and AMICA in four individual subjects (no. 5, 7, 8, and 10; cf. ) for frequency bands 1–4, 15–25, and 60–90 Hz, relative to the power level of the AMICA pipeline. Red indicates areas where the SOBI-FastICA pipeline resulted in a larger reduction of signal power, whereas blue denotes areas where AMICA resulted in a larger reduction of signal power.

    Journal: Frontiers in Neuroscience

    Article Title: Removing ocular artifacts from magnetoencephalographic data on naturalistic reading of continuous texts

    doi: 10.3389/fnins.2022.974162

    Figure Lengend Snippet: Difference between power reduction by SOBI-FastICA and AMICA in four individual subjects (no. 5, 7, 8, and 10; cf. ) for frequency bands 1–4, 15–25, and 60–90 Hz, relative to the power level of the AMICA pipeline. Red indicates areas where the SOBI-FastICA pipeline resulted in a larger reduction of signal power, whereas blue denotes areas where AMICA resulted in a larger reduction of signal power.

    Article Snippet: Therefore, the version of FastICA used in this study [FastICA for Matlab 2.5 ( )] and introduced by strives to obtain an independence-maximizing transformation by seeking one that minimizes the mutual information of the components.

    Techniques:

    Individual variation of power reduction by SOBI-FastICA vs. AMICA in the six parcel and frequency band combinations for which the group-level difference between the methods exceeded 0.05. All such parcels were found in the right hemisphere. The results have been normalized by dividing them with the amount of power reduced by AMICA. Positive sign indicates that SOBI-FastICA reduced more power and negative sign that AMICA did. The colors denote the different parcel–frequency combinations. The colors were created with the linspacer function written by which uses colors derived mainly from ColorBrewer 2.0 .

    Journal: Frontiers in Neuroscience

    Article Title: Removing ocular artifacts from magnetoencephalographic data on naturalistic reading of continuous texts

    doi: 10.3389/fnins.2022.974162

    Figure Lengend Snippet: Individual variation of power reduction by SOBI-FastICA vs. AMICA in the six parcel and frequency band combinations for which the group-level difference between the methods exceeded 0.05. All such parcels were found in the right hemisphere. The results have been normalized by dividing them with the amount of power reduced by AMICA. Positive sign indicates that SOBI-FastICA reduced more power and negative sign that AMICA did. The colors denote the different parcel–frequency combinations. The colors were created with the linspacer function written by which uses colors derived mainly from ColorBrewer 2.0 .

    Article Snippet: Therefore, the version of FastICA used in this study [FastICA for Matlab 2.5 ( )] and introduced by strives to obtain an independence-maximizing transformation by seeking one that minimizes the mutual information of the components.

    Techniques: Derivative Assay

    Systematic group-level differences (two-sided t -test, p ≤ 0.005, uncorrected) in power reduction by SOBI-FastICA and AMICA. P -values illustrated. The “(no significant results)” texts indicate that no significant results were found on the given hemisphere on the given frequency band. Orange indicates areas where the SOBI-FastICA pipeline reduced more power, and blue areas where AMICA reduced more power.

    Journal: Frontiers in Neuroscience

    Article Title: Removing ocular artifacts from magnetoencephalographic data on naturalistic reading of continuous texts

    doi: 10.3389/fnins.2022.974162

    Figure Lengend Snippet: Systematic group-level differences (two-sided t -test, p ≤ 0.005, uncorrected) in power reduction by SOBI-FastICA and AMICA. P -values illustrated. The “(no significant results)” texts indicate that no significant results were found on the given hemisphere on the given frequency band. Orange indicates areas where the SOBI-FastICA pipeline reduced more power, and blue areas where AMICA reduced more power.

    Article Snippet: Therefore, the version of FastICA used in this study [FastICA for Matlab 2.5 ( )] and introduced by strives to obtain an independence-maximizing transformation by seeking one that minimizes the mutual information of the components.

    Techniques:

    Group-level mean differences in power reduction by SOBI-FastICA and AMICA for parcel-frequency band combinations containing points with systematic group-level differences (cf. <xref ref-type= Figure 6 )." width="100%" height="100%">

    Journal: Frontiers in Neuroscience

    Article Title: Removing ocular artifacts from magnetoencephalographic data on naturalistic reading of continuous texts

    doi: 10.3389/fnins.2022.974162

    Figure Lengend Snippet: Group-level mean differences in power reduction by SOBI-FastICA and AMICA for parcel-frequency band combinations containing points with systematic group-level differences (cf. Figure 6 ).

    Article Snippet: Therefore, the version of FastICA used in this study [FastICA for Matlab 2.5 ( )] and introduced by strives to obtain an independence-maximizing transformation by seeking one that minimizes the mutual information of the components.

    Techniques: