Simulates EEG data representing sensorimotor rhythms. Develops and compares spatial filters for neural source reconstruction.
Real-time BCI platform used to assess performance of: (1) discrete trial vs continuous pursuit BCI training (2) source (EEG source imaging) vs sensor space decoding (3) continuous robotic arm control
Comprehensive MEG pipeline from raw data to source reconstruction.
A solver of EEG/MEG inverse problem using a multivariate auto-regressive model on the source space
Python framework for uncertainty estimation and calibration in EEG/MEG inverse source imaging.
Synthetic radiological plume modeling and sensor-network analysis pipeline with Gaussian plume transport, detector thresholds, source reconstruction, Monte Carlo uncertainty, and MATLAB/Python figures.
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