Source Reconstruction Project for QUAKE LIVE [WIP]
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
Simulates EEG data representing sensorimotor rhythms. Develops and compares spatial filters for neural source reconstruction.
A solver of EEG/MEG inverse problem using a multivariate auto-regressive model on the source space
Comprehensive MEG pipeline from raw data to source reconstruction.
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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