2008 Special Issue: BSMART: A Matlab/C toolbox for analysis of multichannel neural time series

Authors:
Jie Cui;Lei Xu;Steven L. Bressler;Mingzhou Ding;Hualou Liang
Affiliations:
School of Health Information Science, University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, TX 77030, USA;School of Health Information Science, University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, TX 77030, USA;Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA;Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA;School of Health Information Science, University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, TX 77030, USA
Venue:
Neural Networks
Year:
2008

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Abstract

We have developed a Matlab/C toolbox, Brain-SMART (System for Multivariate AutoRegressive Time series, or BSMART), for spectral analysis of continuous neural time series data recorded simultaneously from multiple sensors. Available functions include time series data importing/exporting, preprocessing (normalization and trend removal), AutoRegressive (AR) modeling (multivariate/bivariate model estimation and validation), spectral quantity estimation (auto power, coherence and Granger causality spectra), network analysis (including coherence and causality networks) and visualization (including data, power, coherence and causality views). The tools for investigating causal network structures in respect of frequency bands are unique functions provided by this toolbox. All functionality has been integrated into a simple and user-friendly graphical user interface (GUI) environment designed for easy accessibility. Although we have tested the toolbox only on Windows and Linux operating systems, BSMART itself is system independent. This toolbox is freely available (http://www.brain-smart.org) under the GNU public license for open source development.