sigTOOL: A MATLAB Toolbox for Biological Signals Analysis
This paper describes a software package for processing biological signals that has been designed to overcome this by assisting and promoting the sharing of laboratory-developed software. The package, called sigTOOL, has two components: [1] a software development environment designed to facilitate further programming by the end-user and [2] an application implemented through that environment to make the software available through a user-friendly graphical user interface (GUI). This interface is self-modifying so that end-user developed code will be made available automatically through it. The software has been developed to run in MATLAB, a general-purpose development environment that is already widely used in physiological research (for a list of neuroscience tools for MATLAB, for example, see http://www.neuromax.org/NeuroMap/MatlabTools.htm).
Result data displayed in sigTOOL is readily ported to other applications. (A) shows a Poincaré plot formed from a neural spike-train while (B) shows a waveform cross-correlation based on two, simultaneously recorded dorsal root potentials. In each plot, a section of data has been selected using the mouse and these data are displayed numerically in a table from where they may be copied and pasted to other applications. Automatic export is also supported. In (C), the Poincaré plot has been saved and opened in Adobe Illustrator format. The plot was also converted to a surface in sigTOOL illustrating the density of points and this was exported in PDF format and imported to the Illustrator software. (D) shows the data of B exported to Systat SigmaPlot and redrawn in that package. (E) illustrates distribution fitting in sigTOOL: a normal distribution based on maximum-likelihood parameter estimates is shown fitted to a selected range of bins in an interspike interval distribution. In (F), the declining phase of the 2nd peak in the interval distribution has been selected and an exponential curve has been fitted to the selected data using the EzyFit package (Moisy, 2006) as illustrated in the inset.

