BSMART: a Matlab/C toolbox for analysis of multichannel neural time series BSMART, an acronym of Brain-System for Multivariate AutoRegressive Timeseries, is an open-source software package for analyzing brain circuits. BSMART is a project that was born out of a collaborative research effort between Dr. Hualou Liang at Drexel University, Dr. Steven Bressler at Florida Atlantic University, and Dr. Mingzhou Ding at University of Florida. BSMART can be applied to a wide variety of neuroelectromagnetic phenomena, including EEG, MEG and fMRI data. A unique feature of the BSMART package is Granger causality that can be used to assess causal influences and directions of driving among multiple neural signals. The backbone of the BSMART project is Multivariate AutoRegressive (MAR) analysis that has been long developed for statistical quantification of brain connectivity on different time scales. Based upon a MAR model, a plethora of spectral quantities such as auto power, partial power, coherence, partial coherence, multiple coherence and Granger causality can be immediately derived. The approach has been fruitfully used to characterize, with high spatial, temporal, and frequency resolution, functional relations within large scale brain networks.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Oostenveld, Robert; Fries, Pascal; Maris, Eric; Schoffelen, Jan-Mathijs: Fieldtrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data (2011) ioport
- Yan, Xiaodan: Dissociated emergent-response system and fine-processing system in human neural network and a heuristic neural architecture for autonomous humanoid robots (2010) ioport
- Cui, Jie; Xu, Lei; Bressler, Steven L.; Ding, Mingzhou; Liang, Hualou: BSMART: a Matlab/C toolbox for analysis of multichannel neural time series (2008) ioport
- Ding, Mingzhou; Bressler, Steven L.; Yang, Weiming; Liang, Hualou: Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: Data preprocessing, model validation, and variability assessment (2000)