Resource Report

URL: http://sccn.ucsd.edu/wiki/BCILAB

Proper Citation: BCILAB (RRID:SCR_007013)

Description: Open Source MATLAB toolbox and EEGLAB plugin for the design, prototyping, testing, experimentation with, and evaluation of Brain-Computer Interfaces (BCIs), and other systems in the same computational framework. It facilitates the design and development of new methods for cognitive state estimation and their use in both offline data analysis and real-time applications. BCILAB includes an easily extensible collection of currently over 100 methods from the literature (covering signal processing, machine learning and BCI-specific methods). Aside from supporting advanced BCI research, a special aim of BCILAB is to facilitate the adoption of machine learning and advanced statistical modeling for functional neuroimaging purposes in tandem with the EEGLAB platform. The toolbox offers multiple different interfaces which link to the same backend functionality, including a GUI, scripting support (MATLAB-based), APIs for real-time processing, and a variety of extension component interfaces. MATLAB programming is not strictly necessary, as most BCILAB features can be accessed from the GUI, although it is required for batch scripting and custom extensions. The strength of MATLAB-based software lies in its resources for leading-edge scientific computing, as well as in the good support for rapid prototyping, but BCI systems developed in it can be used for real-time out-of-lab experimentation, and can in principle be deployed without the need for a MATLAB license. However, due to the complexity and overhead of the MATLAB environment, the system is best used as a research platform, and not as a product development environment -- end-user software is ideally re-implemented in a compiled language, after a suitable approach has been identified and extensively tested. The process of identifying and testing an approach involves more than just computation, but also data exploration and investigation - an area which is helped by the deep integration with the EEGLAB platform. In the future, this integration will be further strengthened, bringing rich statistical learning and signal processing into routine EEG analysis workflows. The toolbox has been developed by C. Kothe at the Swartz Center, inspired by the preceding PhyPA BCI toolbox created by C. Kothe and T. Zander at the Chair for Human-Machine Systems, Berlin Institute of Technology.

Abbreviations: BCILAB

Resource Type: source code, software resource

Keywords: brain-computer interface, discriminant analysis, temporal integration, matlab, eeg, meg, electrocorticography, statistical operation, temporal transformation, signal processing, machine learning