Aperture is a Matlab-based toolbox for analysis of EEG data. I originally developed it, with colleagues at UPenn and Vanderbilt, to use machine learning techniques to better understand the patterns of oscillatory activity in scalp EEG. The toolbox makes it simple to run pattern classification analysis on complex time-frequency patterns.
This heatmap shows the results of 1900 individual pattern classification analyses done using Aperture. Each pixel represents the accuracy with which category (face, scene, or object) can be decoded from oscillatory activity at that frequency and time after viewing a picture from that category. The toolbox is designed to organize complex classification analyses like this, including sweeps over multiple dimensions like time and frequency, to better understand what information is contained in EEG activity.
Aperture now provides a range of tools for mass-univariate analysis with support for advanced statistics written in R, as well as flexible plotting tools and the ability to generate PDF reports with many plots, to help organize exploratory analysis. Parallel code execution is supported throughout the toolbox to help greatly speed up analysis. Aperture is designed around simple tools that can be easily combined to create a wide variety of analysis protocols.
For more information, see the documentation on GitHub.
The episodic memory behavioral analysis in Matlab (EMBAM) toolbox performs sophisticated analysis and visualization of free recall data to examine many features of human episodic memory. In addition to standard analyses like recall by serial position, it includes functions for measuring the tendency of participants to cluster recalls by temporal, category, and detailed semantic associations.