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Harvard Electroencephalography Database
Sahar Zafar , Tobias Loddenkemper , Jong Woo Lee , Andrew Cole , Daniel Goldenholz , Jurriaan Peters , Alice Lam , Edilberto Amorim , Catherine Chu , Sydney Cash , Valdery Moura Junior , Aditya Gupta , Manohar Ghanta , Marta Fernandes , Haoqi Sun , Jin Jing , M Brandon Westover
Published: June 15, 2023. Version: 1.0 <View latest version>
When using this resource, please cite:
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Zafar, S., Loddenkemper, T., Lee, J. W., Cole, A., Goldenholz, D., Peters, J., Lam, A., Amorim, E., Chu, C., Cash, S., Moura Junior, V., Gupta, A., Ghanta, M., Fernandes, M., Sun, H., Jing, J., & Westover, M. B. (2023). Harvard Electroencephalography Database (version 1.0). Brain Data Science Platform. https://doi.org/10.60508/7651-3337.
The Harvard EEG Database will encompass data gathered from four hospitals affiliated with Harvard University: Massachusetts General Hospital (MGH), Brigham and Women's Hospital (BWH), Beth Israel Deaconess Medical Center (BIDMC), and Boston Children's Hospital (BCH). The EEG data includes three types:
- rEEG: "routine EEGs" recorded in the outpatient setting.
- EMU: recordings obtained in the inpatient setting, within the Epilepsy Monitoring Unit (EMU).
- ICU: recordings obtained from acutely and critically ill patients within the intensive care unit (ICU).
Electroencephalography (EEG) signals are valuable for diagnosing various conditions such as epilepsy, identifying the causes of encephalopathy, predicting the chances of consciousness recovery in patients with prolonged coma after cardiac arrest, assessing the level of consciousness in patients under anesthesia, and assessing sleep quality, among many other medical applications. This repository offers a large and diverse collection of real-world EEG data, serving as a resource for researchers to develop more effective methods for analyzing EEG signals. By sharing this data, our goal is to facilitate broader access to accurate EEG interpretation worldwide and foster research advancements in neurologic disorders. We hope that this repository will lead to broader access to EEG and to new knowledge that improves brain health for all.
Most EEG data in this repository is recorded using the International 10-20 system for scalp electrode placement. Sampling rates of recordings are provided in the EEG header files.
Data is currently stored in .mat format. In the future we will be transferring data to the EEG-BIDS format.
Code for loading the EEG data is available in the associated GitHub repository (https://github.com/bdsp-core/Harvard-EEG-Database-Tools).
In this dataset, all data were anonymized with all identifiable patient information removed.
Thanks to the EEG technologists, attending physicians, and fellows who provide EEG diagnostic services.
Conflicts of Interest
This work was supported by grants from the NIH (R01NS102190, R01NS102574, R01NS107291, RF1AG064312, RF1NS120947, R01AG073410, R01HL161253, R01NS126282, R01AG073598).
- sign the data use agreement for the project