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bsmart matlab toolbox (MathWorks Inc)

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MathWorks Inc bsmart matlab toolbox
Bsmart Matlab 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/product/bsmart+matlab+toolbox/pmc07906351-148-57-60
Average 90 stars, based on 1 article reviews

bsmart matlab toolbox - by Bioz Stars, 2026-10

90/100 stars

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Article Title: Conflict processing networks: A directional analysis of stimulus-response compatibilities using MEG
Article Snippet: Three statistical tests were applied to ensure the validity of the MVAR model: (1) The Durbin-Watson test was used to check if the MVAR residuals were white ( p < 0.05). (2) The stability test was used to check that the VAR coefficients defined a covariance-stationary process [ ], along with the stability index (SI) from the BSMART (version 0.5) Matlab toolbox.

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Article Title: Altered oscillation patterns and connectivity during picture naming in autism
Article Snippet: .. In order to evaluate directional functional connectivity between our regions of interest in the frequency domain, we computed frequency domain Granger causality using the Fieldtrip connectivity analysis functions , which first involved an autoregressive model fit to the data using the bsmart matlab toolbox ( ). ..

Article Title: Preliminary Neurophysiological Evidence of Altered Cortical Activity and Connectivity With Neurologic Music Therapy in Parkinson's Disease
Article Snippet: .. In order to evaluate directional functional connectivity between our regions of interest in the frequency domain, we computed frequency domain coherence using the Fieldtrip connectivity analysis functions (Oostenveld et al., ), which first involved an autoregressive model fit to the data using the bsmart matlab toolbox (Cui et al., ). ..

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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

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