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bsmart toolbox for  (MathWorks Inc)


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    MathWorks Inc bsmart toolbox for
    Bsmart Toolbox For, 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/bsmart+toolbox/pm37703590-114-1-6
    Average 90 stars, based on 1 article reviews
    bsmart toolbox for - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    other:

    Article Title: Interacting humans use forces in specific frequencies to exchange information by touch
    Article Snippet: We used the BSmart toolbox in Matlab ® , and specifically the functions for bivariate analysis by Cui et al. , for our analysis.

    Article Title: The mediodorsal pulvinar coordinates the macaque fronto-parietal network during rhythmic spatial attention
    Article Snippet: For MVAR modeling, with the BSMART toolbox for MATLAB , we used a model order of 8, which generally corresponded to the first Akaike information criterion value.

    Article Title: Dorsal visual stream is preferentially engaged during externally guided action selection in Parkinson Disease.
    Article Snippet: MVAR was calculated using the ‘armorf’ function from MATLAB bsmart toolbox (Cui et al., 2008), over 400 ms moving windows in steps of 10 ms (consistent with the dwPLI analysis).

    Article Title: Relationship between cortical state and spiking activity in the lateral geniculate nucleus of marmosets
    Article Snippet: The timing of LGN cell spike rate and V1 delta frequency power was subjected to Granger causality analysis using the bsmart toolbox for Matlab (Cui et al . 2008 ).

    Article Title: Relationship between cortical state and spiking activity in the lateral geniculate nucleus of marmosets
    Article Snippet: The timing of LGN cell spike rate and V1 delta frequency power was subjected to Granger causality analysis using the bsmart toolbox for Matlab (Cui et al., 2008).



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    Functional block diagram of <t>BSMART</t> showing the main computational blocks and the algorithmic options. The core tools are shown in the dash-edged boxes. Different routines of the tools and help demonstrations can be invoked either from GUI environment <t>or</t> <t>Matlab</t> command window.
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    Functional block diagram of BSMART showing the main computational blocks and the algorithmic options. The core tools are shown in the dash-edged boxes. Different routines of the tools and help demonstrations can be invoked either from GUI environment or Matlab command window.

    Journal:

    Article Title: BSMART: A Matlab /C toolbox for analysis of multichannel neural time series

    doi: 10.1016/j.neunet.2008.05.007

    Figure Lengend Snippet: Functional block diagram of BSMART showing the main computational blocks and the algorithmic options. The core tools are shown in the dash-edged boxes. Different routines of the tools and help demonstrations can be invoked either from GUI environment or Matlab command window.

    Article Snippet: We have described BSMART, a new open source Matlab /C toolbox for the spectral analysis of multichannel neural time series.

    Techniques: Functional Assay, Blocking Assay

    Sample BSMART processing script. This script is an example of preprocessing steps. The Matlab data ‘dat’ (saved as ‘test71.mat’) contains the sample data set (described in the User's Guide and available for download). Script 1 loads the saved data set into Matlab working space. Script 2 and Script 3 complete the preprocessing steps proposed by Ding et al. ( Ding et al., 2000 ). Specifically, function pre_subt_divs() in Script 2 removes the temporal mean from each LFP trial and divides it by the temporal standard deviation (Step (i) in Ding et al.). Function pre_sube_divs() in Script 3 performs the same action but uses ensemble mean and ensemble standard deviation (Steps (ii) and (iii)). Finally, Script 4 plots the waveforms of the preprocessed data set by calling the sigplot() drawing function with a parameter of sampling rate ‘fs’. Function sigplot() is modified from eegplot() in EEGLAB toolbox ( Delorme & Makeig, 2004 ). Note the permutation operation performed before calling sigplot() . This is because sigplot() requires that the format of data be “channels×points× trials”, but ‘dat’ is in the format of “points×channels×trials”.

    Journal:

    Article Title: BSMART: A Matlab /C toolbox for analysis of multichannel neural time series

    doi: 10.1016/j.neunet.2008.05.007

    Figure Lengend Snippet: Sample BSMART processing script. This script is an example of preprocessing steps. The Matlab data ‘dat’ (saved as ‘test71.mat’) contains the sample data set (described in the User's Guide and available for download). Script 1 loads the saved data set into Matlab working space. Script 2 and Script 3 complete the preprocessing steps proposed by Ding et al. ( Ding et al., 2000 ). Specifically, function pre_subt_divs() in Script 2 removes the temporal mean from each LFP trial and divides it by the temporal standard deviation (Step (i) in Ding et al.). Function pre_sube_divs() in Script 3 performs the same action but uses ensemble mean and ensemble standard deviation (Steps (ii) and (iii)). Finally, Script 4 plots the waveforms of the preprocessed data set by calling the sigplot() drawing function with a parameter of sampling rate ‘fs’. Function sigplot() is modified from eegplot() in EEGLAB toolbox ( Delorme & Makeig, 2004 ). Note the permutation operation performed before calling sigplot() . This is because sigplot() requires that the format of data be “channels×points× trials”, but ‘dat’ is in the format of “points×channels×trials”.

    Article Snippet: We have described BSMART, a new open source Matlab /C toolbox for the spectral analysis of multichannel neural time series.

    Techniques: Standard Deviation, Sampling, Modification