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

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