Nooshin Bahador

DIME improves SOZ localization using iEEG data, achieving high recall and accuracy in predicting surgical outcomes.

The paper semi-automatically annotates ictal chirps in iEEG data from epilepsy patients and finds their spectro-temporal features correlate with seizure onset zones and surgical outcomes.

The paper develops MATLAB-based software to automatically identify and analyze theta rhythms in hippocampal EEG/LFP data, showing it can robustly detect feature changes during seizures and post-seizure periods.

This study identifies chirp patterns in ictal discharges as a potential biomarker for tracking seizure progression in a mouse epilepsy model.

This paper introduces an SVD-based adaptive filtering method to remove both fast and slow artifacts from local field potentials during DBS, outperforming existing techniques on synthetic and real data.