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Read Me.txt
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Read Me.txt
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Source-Informed Segmentation Tool (SISTool) toolbox for MATLAB:
---------------------------------------------------------------
SISTool is an open-source toolbox for segmenting EEG data using the source-informed segmentation algorithm
proposed in [1-4].
Installing SISTool:
-------------------
To install SISTool toolbox, add the SISTool folder to MATLAB's search paths.
Running SISTool:
----------------
To run the toolbox, type SISTool (make sure to capitalize the first letters).
Data Format:
------------
The native data format of SISTool toolbox consists in a structure with the following fields:
data - An array of dimentions (#channels * #frames * #trials) containing the EEG data. This
field is necessary.
segpnts - A cell vector of length (#trials) with each cell containing a vector of length (#segs)
giving the beginning segment boundaries in [msec]. The first segment boundary is always
0.
start_time - The starting time of all EEG signals in [msec].
srate - The sampling rate of the EEG data in [Hz].
Wr - The reference window in [msec].
Wd - The decision window in [msec].
Ws - The sliding window in [msec]. This field may be empty to indicate the default value of
(Ws = Wr).
Wp - The step in [msec]. This field may be empty to indicate the default value of
(Wp = 1/srate).
Wv - The overlap in [msec]. This field may be empty to indicate the default value of (Wv = 0).
time - The time line in [msec].
SISTool can import/export data in the native format from MATLAB's workspace. SISTool can also load/save
data in the native format as .mat files. The data structure in loaded/saved data is always named "EEG".
SISTool can also import arrays with dimensions defined as in the structure field "data" directly from
MATLAB's workspace.
References:
-----------
[1] Ali E. Haddad and Laleh Najafizadeh, "Source-informed segmentation: A data-driven
approach for the temporal segmentation of EEG," IEEE Transactions on Biomedical
Engineering, vol. 66, no. 5, pp. 1429-1446, 2019.
[2] Ali E. Haddad, Laleh Najafizadeh, "Global EEG segmentation using singular value
decomposition," 37th Annual International Conference of the IEEE Engineering
in Medicine and Biology Society (EMBC), IEEE, 2015, pp. 558-561.
[3] Ali Haddad, Laleh Najafizadeh, "Multi-scale analysis of the dynamics of brain
functional connectivity using EEG," IEEE Biomedical Circuits and Systems
Conference (BioCAS), IEEE, 2016, pp. 240-243.
[4] Ali Haddad, Laleh Najafizadeh, "Source-informed segmentation: Towards capturing
the dynamics of brain functional networks through EEG," 50th Asilomar Conference
on Signals, Systems and Computers, IEEE, 2016, pp. 1290-1294.