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