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Biomedical Functional Imaging and Neuroengineering Laboratory

College of Science & Engineering > Dept. of Biomedical Engineering


The following software can be downloaded by clicking the links:


Description eConnectome is an open-source MATLAB software package for imaging brain functional connectivity from electrophysiological signals. It provides interactive graphical interfaces for EEG/ECoG preprocessing, source estimation, connectivity analysis and visualization. The current release allows connectivity imaging from ECoG and EEG over sensor and source domains. This package is designed for use by researchers in neuroscience, psychology, cognitive science, clinical neurophysiology, neurology and other disciplines. The graphical interface-based platform requires little programming knowledge or experience with MATLAB.

Cortical imaging using 3-spherical head volume conductor model

Description This program computes the scalp potential distribution generated by one, multiple or distributed current dipoles via modeling the head volume conductor as three concentric spherical layers, and can also be used to estimate cortical activities from scalp potentials in the 3-spheres head model (Wang & He, IEEE Trans. BME, 1998; He et al, Human Brain Mapping, 2001). The conductivities of the scalp and the brain are assumed to be identical, and the skull-to-brain conductivity ratio is assumed as 20 (Zhang et al., Applied Phy. Lett., 89: 223903, 2006). As the three-spherical model is at most an approximation of the head volume conductor, it fails to consider the individual head geometries and conductivities. However, this model possesses the major physical property of the head volume conduction. Therefore, it is an appropriate head model in many computer simulation studies with respect to the EEG inverse problem; it is even still used in dealing with experimental data for EEG source imaging/localization, although the modeling inaccuracy may be of concern.

Space-Time-Frequency (STF) approach for EEG classification for BCI Applications

Description This program computes the STF synthesis pattern of EEG signals and classifies it into motor imagery tasks (Wang, Deng, He, Clinical Neurophysiology, 2004). The EEGs are decomposed into a series of frequency bands, and the instantaneous power is represented by the envelop of oscillatory activity, which forms the spatial patterns for a given electrode montage at a time-frequency grid. Time-frequency weights determined by training process are used to synthesize the contributions from the time- frequency domains. The present method promises to provide a useful alternative as a general purpose classification procedure for motor imagery classification.