Interictal high-density scalp EEG in focal epilepsy patients

This dataset includes de-identified interictal spike information in 20 focal epilepsy patients who became seizure-free after surgery. This dataset has been used and analyzed to study epilepsy sources and the results are reported in: Sun R, Sohrabpour A, Worrell GA, He B: “Deep Neural Networks Constrained by Neural Mass Models Improve Electrophysiological Source Imaging of Spatiotemporal Brain Dynamics.” <em>Proceedings of the National Academy of Sciences of the United States of America</em> 119.31 (2022): e2201128119. <br> Please cite the above paper if you use any data included in this dataset. Codes related to this study are also available from (https://github.com/bfinl/DeepSIF). Clinical information of the patients are described in the Supplementary Table S1 of the paper. This dataset was collected under support from the National Institutes of Health via grants NS096761 and EB021027 to Dr. Bin He and Dr. Greg Worrell. The human data were collected and de-identified as part of NIH funded research at Mayo Clinic, Rochester, overseen by Dr. Greg Worrell, and processed and organized at Dr. Bin He’s lab at Carnegie Mellon University. The data are shared for information only. The EEG recorded in this dataset follows a 10-10 system and contains 76 channels of recording. One channel is the reference channel and given that we use a common reference channel for source imaging this channel is removed from the topographical map data and the corresponding rows of the lead-field matrix for each patient, equals 75. The EEG data were filtered between 1-40 Hz and a common average reference was used to pre-process the data. <br> The data include three variables: * spike_peak_data: Contains the topographical EEG map at the peak of the spike selected in each individual patient. * patient_id: patient index corresponding to the patient ID in Supplementary Table S1 of the paper. * eloc: electrode information in EEGLAB (https://sccn.ucsd.edu/eeglab/index.php) format. <br>