An EEG dataset for interictal epileptiform discharge with spatial distribution information

This dataset contains annotated interictal epileptiform discharge (IED) from 84 patients (Peking Union Medical College Hospital, China), each contributing 20 minutes of continuous raw EEG recordings, using MAT format. The IEDs are categorized into five types based on occurrence regions. The states of consciousness (wake/sleep) are annotated.
Note on version 2:The dataset has been updated. Specifically, a total of 37 annotations (31 deletions and 6 updates) were adjusted to ensure accuracy and consistency for analysis. These annotations were modified due to their atypical characteristics, differing from conventional interictal epileptiform discharges (IEDs). This refined dataset represents the final version used for training and validation in our associated paper entitled “An EEG dataset for interictal epileptiform discharges with spatial distribution information”.
The specific changes of annotations are listed as follow:MAT_Files:DA00103C.mat  Delete:['305.846', '0', '!']DA00100Z.mat  Delete:['142.686', '0', '!']DA00102T.mat  Delete:['213.124', '0', '!'], ['388.27', '0', '!'], Update:['213.78', '0', '!end'] -> ['211.78', '0', '!end']DA00102W.mat  Delete:['48.274', '0', '!'], ['438.406', '0', '!'], ['516.94', '0', '!'], ['576.554', '0', '!']DA00102Y.mat  Delete:['605.436', '0', '!']DA00103B.mat  Delete:['1173.344', '0', '!']DA00103I.mat  Update:['1128.746', '0', '!end'] -> ['1127.746', '0', '!end']DA00103K.mat  Delete:['485.026', '0', '!']DA00103M.mat  Delete:['45.006', '0', '!'], ['76.166', '0', '!'], ['108.226', '0', '!'], ['189.608', '0', '!'], ['537.642', '0', '!']DA00103N.mat  Update:['1196.27', '0', '!end'] -> ['1195.27', '0', '!end']DA00103Q.mat  Delete:['696.692', '0', '!'], ['1213.206', '0', '!'], Update:['632.1', '0', '!'] -> ['632.2', '0', '!']DA00103U.mat  Delete:['1076.12', '0', '!'], ['1208.146', '0', '!'], ['1210.474', '0', '!'], ['1211.242', '0', '!']DA00100S.mat  Delete:['1204.8', '0', '!']DA00103O.mat  Delete:['12.542', '0', '!']DA00103S.mat  Delete:['1173.22', '0', '!']DA001010.mat  Delete:['1185.496', '0', '!']DA001031.mat  Delete:['0.684', '0', '!'], ['222.106', '0', '!']DA00103E.mat  Delete:['862.716', '0', '!']DA00100V.mat  Delete:['768.154', '0', '!']，['768.532', '0', '!']DA00102R.mat  Update:['552.244', '0', '!end'] →['551.244', '0', '!end']，['704.172', '0', '!end'] →['703.172', '0', '!end']
The changes in MAT_Files result in alterations in the npy_files: DA00103C_152000_154000_500__5.npy(Occipital-IED) -> DA00103C_152000_154000_500__0.npy(Non-IED)DA00100Z_70000_72000_500__2.npy(Frontal-IED) -> DA00100Z_70000_72000_500__0.npy (Non-IED)DA00102W_24000_26000_500__3.npy(Temporal-IED) -> DA00102W_24000_26000_500__0.npy (Non-IED)DA00102W_218000_220000_500__3.npy(Temporal-IED) -> DA00102W_218000_220000_500__0.npy (Non-IED)DA00102W_258000_260000_500__3.npy(Temporal-IED) -> DA00102W_258000_260000_500__0.npy(Non-IED)DA00102Y_302000_304000_500__4.npy(Centro-Parietal-IED) -> DA00102Y_302000_304000_500__0.npy(Non-IED)DA00103M_22000_24000_500__2.npy(Frontal-IED) -> DA00103M_22000_24000_500__0.npy(Non-IED)DA00103M_94000_96000_500__2.npy(Frontal-IED) -> DA00103M_94000_96000_500__0.npy(Non-IED)DA00103M_268000_270000_500__2.npy(Frontal-IED) -> DA00103M_268000_270000_500__0.npy(Non-IED)DA00103U_604000_606000_500__3.npy(Temporal-IED) -> DA00103U_604000_606000_500__0.npy(Non-IED)DA00103C_170000_172000_500__0.npy(Non-IED) -> DA00103C_170000_172000_500__5.npy(Occipital-IED)DA00100Z_0_2000_500__0.npy(Non-IED) -> DA00100Z_0_2000_500__2.npy(Frontal-IED)DA00102W_124000_126000_500__0.npy(Non-IED) -> DA00102W_124000_126000_500__3.npy(Temporal-IED)DA00102W_242000_244000_500__0.npy(Non-IED) -> DA00102W_242000_244000_500__3.npy(Temporal-IED)DA00102W_268000_270000_500__0.npy(Non-IED) -> DA00102W_268000_270000_500__3.npy(Temporal-IED)DA00102Y_612000_614000_500__0.npy(Non-IED) -> DA00102Y_612000_614000_500__4.npy(Centro-Parietal-IED)DA00103M_30000_32000_500__0.npy(Non-IED) -> DA00103M_30000_32000_500__2.npy(Frontal-IED)DA00103M_96000_98000_500__0.npy(Non-IED) -> DA00103M_96000_98000_500__2.npy(Frontal-IED)DA00103M_568000_570000_500__0.npy(Non-IED) -> DA00103M_568000_570000_500__2.npy(Frontal-IED)DA00103U_606000_607500_500__0.npy(Non-IED) -> DA00103U_606000_607500_500__3.npy(Temporal-IED)