SISTool is an open-source toolbox for segmenting EEG data using the source-informed segmentation algorithm proposed in [1-4].
To install SISTool toolbox, add the SISTool folder to MATLAB's search paths.
To run the toolbox, type SISTool (make sure to capitalize the first letters).
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.
[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.