Resources

Harvard Electroencephalography Database

iOS-app-based EEG education and gamified learning data: 2,270 participants rated candidate interictal epileptiform discharges (IEDs) on the DiagnosUs app, with 13,262 EEG candidates, survey responses, and learning curves.

Code and curated polysomnography dataset (10,784 PSGs from 8,044 patients, MGB Sleep Lab 2009-2019) used in Ye et al. 2023 (Sleep) to detect dementia and MCI from sleep EEG features.

Dataset and code for developing and validating the EICANS score, a machine learning–derived EEG metric for automated detection and grading of immune effector cell-associated neurotoxicity syndrome (ICANS) in patients receiving CAR-T cell therapy.

Data and code for a bi-institutional randomized controlled educational pilot trial (n=21 junior neurology residents) evaluating a web- and app-based training program for identifying interictal epileptiform discharges (IEDs) on EEG.

"Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs" and "Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation"

Data and code to reproduce results in "Continuous Electroencephalography Predicts Delayed Cerebral Ischemia after Subarachnoid Hemorrhage: A Prospective Study of Diagnostic Accuracy"

Initial version

BIND Database 1: Neuroimaging Data (MRI, CT, PET, SPECT) that can be paired with EEG and PSG (found in the Harvard EEG Database https://bdsp.io/content/harvard-eeg-db/4.1/). LLMs helped categorize pathology.

De-identified polysomnography dataset and analysis code from Ye et al. 2020 (JAMA Netw Open): sleep-EEG-based Brain Age Index (BAI) computed across 9,834 MGH Sleep Lab PSGs and tested as a biomarker for dementia and MCI versus healthy controls.