T. Limpiti, B.D. Van Veen, H.T. Attias, and S.S. Nagarajan, A spatio-temporal framework for estimating trial-to-trial amplitude variation in event-related MEG/EEG, IEEE Trans. Biomedical Engineering, vol. 56, pp. 633-645, March 2009.
MATLAB Software and Examples
- m-file that calculates LDSR amplitude estimates, IR amplitude estimates, and the constant response solution: AmplitudeVarEstimation
- MATLAB subroutines required by AmplitudeVarEstimation.m: subroutines
- .mat file containing data, spatial bases, and temporal bases to reproduce Example 2 in the paper for the smooth amplitude variation scenario. See Fig. 6a,b. Also includes the true space-time signal (Signal.waveform) and amplitude evolution (Signal.amplitude) used to simulate the data for comparison to the estimated quantities: Example
- m-file that determines the patch bases for spatial representation of an extended source given a collection of forward models and defined patch extent.
(see T. Limpiti, B.D. Van Veen, and R.T. Wakai, Cortical Patch Basis Model for Spatially Extended Neural Activity, IEEE Trans. Biomedical Engineering, vol. 53, pp. 1740- 1754, Sept. 2006.): SpatialBasesGen
- m-file that determines temporal bases for a bandlimited signal of fixed duration and specified bandwidth. (see the appendix in B.V. Baryshnikov, B.D. Van Veen, and R.T. Wakai, Maximum likelihood estimation of low rank signals for multiepoch MEG/EEG analysis, IEEE Trans. BiomedicalEngineering, vol. 51, pp. 1981-1993, Nov. 2004.): TemporalBasesGen