PyMVPA is a Python module intended to ease pattern classification analyses of large datasets. In the neuroimaging contexts such analysis techniques are also known as decoding or MVPA analysis. PyMVPA provides high-level abstraction of typical processing steps and a number of implementations of some popular algorithms. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run.
Although a backup of my repository is available here, it is highly recommended that you checkout the full project at www.pymvpa.org.
MatlabDispatch is a solution to quickly control one Matlab session from another. It began when I was dissatisfied with the performance of the ActiveX protocol for communicating with the Matlab Automation Server; I wanted a way to simultaneously control two Matlab sessions (on a shiny new Core2Duo) and circumvent the single-threaded nature of Matlab.
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V1 is an integrated Matlab solution for visual stimulus presentation, data acquisition, and behavioral experiment control. It was primarily created to replicate the functionality of programs such as Cortex, by the Salk Institute, in the much more accessible Matlab environment (hence, V1 is like a ‘Matlab Cortex’). The primary advantages of V1 include:
- Visual stimulus presentation based on the much-used, open source, and well supported Psychtoolbox (www.psychtoolbox.org).
- Data acquisition relying on Mathwork’s Data Acquisition Toolbox, allowing integrated, flexible, realtime data acquisition dependent only on your hardware.
- Experimental control within Matlab, allowing for experimentation and data analysis within the same package – whether post hoc or in realtime.
V1, simply, is a wrapper class and helper functions that facilitate the goals of experimental control while relying on highly established, standard Matlab extensions to provide the greatest control and functionality any experiment might require.
It is publicly available for download from the V1.git repository. Dependencies: Matlab 7.6+ (earlier versions require the older class model, which is in the development history, but is not recommended), PTB, and optionally the DAQ toolbox. Installation: add the root directory to your Matlab path, as well as the ‘helpers’, ‘localMachine’ and optionally ‘experiments’ subdirectories. For HUD functionality you must add the HUD.jar to your Java classpath as noted in java/v1Java.m. Once installed, start by typing ‘help V1′, follow the instructions to configure your local machine, and begin experimenting!