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Meta-EEGs: A structured approach for processing high-volume EEG data.

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3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches
  1. [1] § Method details › Meta-EEG of Siena Scalp EEG Database v1.0.0 ↔ code_siena.ipynb, lines 107–190 · score 0.58 · Siena DB, post ictal, lateralisation, localization, subfile, row
  2. [2] § Method details › Meta-EEG of CHB-MIT Scalp EEG Database v1.0.0.0 ↔ data_preprocess_chbmit.py, lines 11–27 · score 0.51 · CHB MIT DB, timestamp.csv, row
  3. [3] § Method details › Meta-EEG of CHB-MIT Scalp EEG Database v1.0.0.0 ↔ code_chbmit.ipynb, lines 13–26 · score 0.51 · CHB MIT DB, timestamp.csv, patients

Paper

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The authors' code

Jupyter notebook · 360 lines · 9.2 KB · no license · 1 match

  1. # %%
  2. pip install pyEDFlib
  4. # %%
  5. # libraries installation
  6. from pyedflib import EdfReader
  7. from numpy import zeros, arange
  8. import csv
  9. import pandas as pd
  10. from csv import reader, writer
  11. import os
  13. # %%
  14. #paths defines
  15. Edfpath='edf_files/'
  16. tempath='temp/'
  18. # read the excel file made for patient data
  19. excelData=pd.read_csv('Siena DB timestamp.csv', header=None)
  20. total_no_of_edfs=50
  21. excelData
  23. # %% [markdown]
  24. # **ICTAL**
  26. # %%
  27. seizuren= 0
  28. for i in range(1,total_no_of_edfs):
  30. edfFile=excelData[1][i]
  31. patientno= excelData[0][i]
  32. subfile=excelData[1][i]
  33. seizure_type= excelData[4][i]
  34. localization=excelData[5][i]
  35. lateralization=excelData[6][i]
  38. # read Ictal start and end rows
  39. IctalS= int(excelData[15][i])
  40. IctalE= int(excelData[16][i])
  42. print(seizure_type,localization,lateralization,IctalS,IctalE)
  44. # read edf file
  45. f=EdfReader(Edfpath+ edfFile+'.edf')
  46. n = f.signals_in_file
  47. signal_labels = f.getSignalLabels()
  48. print(signal_labels)
  49. data = zeros((n,f.getNSamples()[0]))
  50. data
  51. for k in arange(n):
  52. data[k, :] = f.readSignal(k)
  54. #store all data
  55. data_eeg = data
  57. #close the edf file
  58. f._close()
  59. del f
  60. print(data_eeg.shape)
  62. df = pd.DataFrame(data_eeg)
  64. # convert the edf to csv file
  65. df.to_csv(tempath+'temp1.csv', index=False)
  66. total_cols= len(df.columns)
  67. print(total_cols)
  69. #cut the data for ictal
  70. cols=list(range(0,IctalS))
  71. df.drop(columns=cols, axis=1,inplace=True)
  72. cols=list(range(IctalE+1,total_cols))
  73. df.drop(columns=cols, axis=1,inplace=True)
  74. print(df.shape)
  76. # add class label
  77. df[len(df.columns)]=1
  79. #save the updated csv file
  80. df.to_csv(tempath+'temp2.csv', index=False)
  83. # create pateint specific folder
  84. folderpath = 'Generated siena db/Patient Inter Specific/'+seizure_type+'/'+localization+'/'+lateralization+'/'+'PN'+str(patientno)+'/'
  85. if not os.path.exists(folderpath):
  86. os.makedirs(folderpath)
  88. newpath= folderpath + 'Ictal' +'/'
  89. if not os.path.exists(newpath):
  90. os.makedirs(newpath)
  91. print(df.shape)
  92. # transpose the csv
  93. with open(tempath+'temp2.csv') as f, open(newpath + subfile +'_ictal.csv', 'w') as fw:
  94. writer(fw, delimiter=',').writerows(zip(*reader(f, delimiter=',')))
  95. df=pd.read_csv(newpath + subfile +'_ictal.csv', header=None)
  96. df.drop(columns=0, axis=1,inplace=True)
  97. print(df.shape)
  99. # Patient Non Specific
  100. newpath2= 'Generated siena db/Patient Non-Specific/Ictal/'
  101. if not os.path.exists(newpath2):
  102. os.makedirs(newpath2)
  104. df.to_csv(newpath + subfile +'_ictal.csv', index=False, header=False)
  105. df.to_csv(newpath2+str(i+seizuren)+'.csv', index=False, header=False)
  107. # %% [markdown]
  108. # **POST-ICTAL**
  110. # %%
  111. seizuren= 0
  112. for i in range(1,total_no_of_edfs):
  114. edfFile=excelData[1][i]
  115. patientno= excelData[0][i]
  116. subfile=excelData[1][i]
  117. seizure_type= excelData[4][i]
  118. localization=excelData[5][i]
  119. lateralization=excelData[6][i]
  122. # read Post Ictal start and end rows
  123. PostIctalS= int(excelData[19][i])
  124. PostIctalE= int(excelData[20][i])
  126. print(seizure_type,localization,lateralization,PostIctalS,PostIctalE)
  128. # read edf file
  129. f=EdfReader(Edfpath+ edfFile+'.edf')
  130. n = f.signals_in_file
  131. signal_labels = f.getSignalLabels()
  132. print(signal_labels)
  133. data = zeros((n,f.getNSamples()[0]))
  134. data
  135. for k in arange(n):
  136. data[k, :] = f.readSignal(k)
  138. #store all data
  139. data_eeg = data
  141. #close the edf file
  142. f._close()
  143. del f
  144. print(data_eeg.shape)
  146. df = pd.DataFrame(data_eeg)
  148. # convert the edf to csv file
  149. df.to_csv(tempath+'temp1.csv', index=False)
  150. total_cols= len(df.columns)
  151. print(total_cols)
  153. #cut the data for post-ictal
  154. cols=list(range(0,PostIctalS))
  155. df.drop(columns=cols, axis=1,inplace=True)
  156. cols=list(range(PostIctalE+1,total_cols))
  157. df.drop(columns=cols, axis=1,inplace=True)
  158. print(df.shape)
  160. # add class label
  161. df[len(df.columns)]=1
  163. #save the updated csv file
  164. df.to_csv(tempath+'temp2.csv', index=False)
  167. # create pateint specific folder
  168. folderpath = 'Generated siena db/Patient Inter Specific/'+seizure_type+'/'+localization+'/'+lateralization+'/'+'PN'+str(patientno)+'/'
  169. if not os.path.exists(folderpath):
  170. os.makedirs(folderpath)
  171. newpath= folderpath + 'Post-Ictal' +'/'
  172. if not os.path.exists(newpath):
  173. os.makedirs(newpath)
  174. print(df.shape)
  175. # transpose the csv
  176. with open(tempath+'temp2.csv') as f, open(newpath + subfile +'_post-ictal.csv', 'w') as fw:
  177. writer(fw, delimiter=',').writerows(zip(*reader(f, delimiter=',')))
  178. df=pd.read_csv(newpath + subfile +'_post-ictal.csv', header=None)
  179. df.drop(columns=0, axis=1,inplace=True)
  180. print(df.shape)
  183. # Patient Non Specific
  184. newpath2= 'Generated siena db/Patient Non-Specific/Post-Ictal/'
  185. if not os.path.exists(newpath2):
  186. os.makedirs(newpath2)
  189. df.to_csv(newpath + subfile +'_post-ictal.csv', index=False, header=False)
  190. df.to_csv(newpath2+str(i+seizuren)+'.csv', index=False, header=False)
  192. # %% [markdown]
  193. # **PRE_ICTAL**
  195. # %%
  196. seizuren= 0
  197. for i in range(1,total_no_of_edfs):
  199. edfFile=excelData[1][i]
  200. patientno= excelData[0][i]
  201. subfile=excelData[1][i]
  202. seizure_type= excelData[4][i]
  203. localization=excelData[5][i]
  204. lateralization=excelData[6][i]
  207. # read Pre Ictal start and end rows
  208. PreIctalS= int(excelData[23][i])
  209. PreIctalE= int(excelData[24][i])
  211. print(seizure_type,localization,lateralization,PreIctalS,PreIctalE)
  213. # read edf file
  214. f=EdfReader(Edfpath+ edfFile+'.edf')
  215. n = f.signals_in_file
  216. signal_labels = f.getSignalLabels()
  217. print(signal_labels)
  218. data = zeros((n,f.getNSamples()[0]))
  219. data
  220. for k in arange(n):
  221. data[k, :] = f.readSignal(k)
  223. #store all data
  224. data_eeg = data
  226. #close the edf file
  227. f._close()
  228. del f
  229. print(data_eeg.shape)
  231. df = pd.DataFrame(data_eeg)
  233. # convert the edf to csv file
  234. df.to_csv(tempath+'temp1.csv', index=False)
  235. total_cols= len(df.columns)
  236. print(total_cols)
  238. #cut the data for pre-ictal
  239. cols=list(range(0,PreIctalS))
  240. df.drop(columns=cols, axis=1,inplace=True)
  241. cols=list(range(PreIctalE+1,total_cols))
  242. df.drop(columns=cols, axis=1,inplace=True)
  243. print(df.shape)
  245. # add class label
  246. df[len(df.columns)]=1
  248. #save the updated csv file
  249. df.to_csv(tempath+'temp2.csv', index=False)
  252. # create pateint specific folder
  253. folderpath = 'Generated siena db/Patient Inter Specific/'+seizure_type+'/'+localization+'/'+lateralization+'/'+'PN'+str(patientno)+'/'
  254. if not os.path.exists(folderpath):
  255. os.makedirs(folderpath)
  256. newpath= folderpath + 'Pre-Ictal' +'/'
  257. if not os.path.exists(newpath):
  258. os.makedirs(newpath)
  259. print(df.shape)
  260. # transpose the csv
  261. with open(tempath+'temp2.csv') as f, open(newpath + subfile +'_pre-ictal.csv', 'w') as fw:
  262. writer(fw, delimiter=',').writerows(zip(*reader(f, delimiter=',')))
  263. df=pd.read_csv(newpath + subfile +'_pre-ictal.csv', header=None)
  264. df.drop(columns=0, axis=1,inplace=True)
  265. print(df.shape)
  268. # Patient Non Specific
  269. newpath2= 'Generated siena db/Patient Non-Specific/Pre-Ictal/'
  270. if not os.path.exists(newpath2):
  271. os.makedirs(newpath2)
  274. df.to_csv(newpath + subfile +'_pre-ictal.csv', index=False, header=False)
  275. df.to_csv(newpath2+str(i+seizuren)+'.csv', index=False, header=False)
  277. # %% [markdown]
  278. # **PERI_ICTAL**
  280. # %%
  281. seizuren= 0
  282. for i in range(1,total_no_of_edfs):
  284. edfFile=excelData[1][i]
  285. patientno= excelData[0][i]
  286. subfile=excelData[1][i]
  287. seizure_type= excelData[4][i]
  288. localization=excelData[5][i]
  289. lateralization=excelData[6][i]
  292. # read Peri Ictal start and end rows
  293. PeriIctalS= int(excelData[27][i])
  294. PeriIctalE= int(excelData[28][i])
  296. print(seizure_type,localization,lateralization,PeriIctalS,PeriIctalE)
  298. # read edf file
  299. f=EdfReader(Edfpath+ edfFile+'.edf')
  300. n = f.signals_in_file
  301. signal_labels = f.getSignalLabels()
  302. print(signal_labels)
  303. data = zeros((n,f.getNSamples()[0]))
  304. data
  305. for k in arange(n):
  306. data[k, :] = f.readSignal(k)
  308. #store all data
  309. data_eeg = data
  311. #close the edf file
  312. f._close()
  313. del f
  314. print(data_eeg.shape)
  316. df = pd.DataFrame(data_eeg)
  318. # convert the edf to csv file
  319. df.to_csv(tempath+'temp1.csv', index=False)
  320. total_cols= len(df.columns)
  321. print(total_cols)
  323. #cut the data for peri-ictal
  324. cols=list(range(0,PeriIctalS))
  325. df.drop(columns=cols, axis=1,inplace=True)
  326. cols=list(range(PeriIctalE+1,total_cols))
  327. df.drop(columns=cols, axis=1,inplace=True)
  328. print(df.shape)
  330. # add class label
  331. df[len(df.columns)]=1
  333. #save the updated csv file
  334. df.to_csv(tempath+'temp2.csv', index=False)
  337. # create pateint specific folder
  338. folderpath = 'Generated siena db/Patient Inter Specific/'+seizure_type+'/'+localization+'/'+lateralization+'/'+'PN'+str(patientno)+'/'
  339. if not os.path.exists(folderpath):
  340. os.makedirs(folderpath)
  341. newpath= folderpath + 'Peri-Ictal' +'/'
  342. if not os.path.exists(newpath):
  343. os.makedirs(newpath)
  344. print(df.shape)
  345. # transpose the csv
  346. with open(tempath+'temp2.csv') as f, open(newpath + subfile +'_peri-ictal.csv', 'w') as fw:
  347. writer(fw, delimiter=',').writerows(zip(*reader(f, delimiter=',')))
  348. df=pd.read_csv(newpath + subfile +'_peri-ictal.csv', header=None)
  349. df.drop(columns=0, axis=1,inplace=True)
  350. print(df.shape)
  353. # Patient Non Specific
  354. newpath2= 'Generated siena db/Patient Non-Specific/Peri-Ictal/'
  355. if not os.path.exists(newpath2):
  356. os.makedirs(newpath2)
  359. df.to_csv(newpath + subfile +'_peri-ictal.csv', index=False, header=False)
  360. df.to_csv(newpath2+str(i+seizuren)+'.csv', index=False, header=False)

code_siena.ipynb at commit d66f64f, no license · at the source

Overview

Authors: Palak Handa1, Manya Joshi2, Esha Gupta3, Ramona Woitek1
ORCID iDs: Palak Handa
  1. Research Center for Medical Image Analysis and Artificial Intelligence, Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
  2. Department of Artificial Intelligence and Data Science, Guru Gobind Singh Indraprastha University, New Delhi, India
  3. Expedia Group, Noida, India
Institutions: Danube Private University (Austria); Universität für Weiterbildung Krems (Austria); Guru Gobind Singh Indraprastha University (India)
Journal: MethodsX, volume 17, article 104005
Dates: received 27 February 2026; accepted 10 June 2026; published online 11 June 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.mex.2026.104005 · PMID 42327638 · PMCID PMC13279890 · OpenAlex W7164299352
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), epilepsy (population)
Methods: Connectivity, Smoothing, state filtering, decompositions, Machine learning, Physiology & signal measures
Keywords: AI-EEG, Automated seizure detection, Automated epilepsy diagnosis, Meta-EEGs
Journal subjects: Neuroscience
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 20 references in the paper

Abstract

Electroencephalography (EEG) is widely used clinically and in research, including AI-driven applications for cognitive state analysis and neurological disorder detection, such as epilepsy. However, automated seizure detection faces challenges, including inconsistent windowing, timestamp misalignment, label-based signal segmentation, and unstructured large-scale EEG data—especially critical in event-driven settings. To address these, we introduce Meta-EEGs, a structured, domain-agnostic EEG representation for temporally labelled tasks. Meta-EEGs provide consistent windowing with precise time alignment, enable event-based segmentation based on annotations or class labels, and organise raw EEG recordings into a simpler, relatively reduced in volume format suitable for AI model input. They also support the creation and management of hierarchical EEG datasets, currently lacking in the field. As case studies, we applied Meta-EEGs to the CHB-MIT and Siena Scalp EEG Databases, generating structured datasets that are publicly available on Figshare and have been downloaded over 2000 times since 2022. The working code is accessible on GitHub.

Main features and applications: • Provides consistent window definition, timestamp alignment, signal segmentation, and standardised structuring for large-scale EEG studies. • Releases two fully annotated, reduced in volume datasets for automated seizure detection, supporting reproducible and generalisable analysis. • Enables AI model development for seizure detection, event classification, patient-specific and cross-patient analysis, and hierarchical EEG tasks without repeated initial preprocessing.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

misahub2023/META-EEG-CHBMIT-and-SIENA

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d66f64fe4d6286272aae4122dc236bf60a01fe28, 28 August 2025
Languages: Jupyter (2), Python (2)
Size: 12 files, 4 scripts
Software Heritage: not archived
Found in: the text, “Method details”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (2 files), pandas (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/misahub2023/META-EEG-CHBMIT-and-SIENA

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 4 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The data is available on Figshare.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Authors: added Palak Handa (0009-0001-4573-3967); removed Palak Handa

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 4 keywords, 4 references.

Cite

This paper

Handa, P., Joshi, M., Gupta, E., & Woitek, R. (2026). Meta-EEGs: A structured approach for processing high-volume EEG data. MethodsX, 17, 104005. https://doi.org/10.1016/j.mex.2026.104005

BibTeX

@article{handa2026meta,
author = {Handa, Palak and Joshi, Manya and Gupta, Esha and Woitek, Ramona},
title = {{Meta-EEGs: A structured approach for processing high-volume EEG data}},
journal = {MethodsX},
year = {2026},
month = jun,
volume = {17},
pages = {104005},
publisher = {Elsevier},
issn = {2215-0161},
doi = {10.1016/j.mex.2026.104005},
url = {https://doi.org/10.1016/j.mex.2026.104005},
pmid = {42327638},
pmcid = {PMC13279890}
}

RIS

TY - JOUR
AU - Handa, Palak
AU - Joshi, Manya
AU - Gupta, Esha
AU - Woitek, Ramona
TI - Meta-EEGs: A structured approach for processing high-volume EEG data
T2 - MethodsX
J2 - MethodsX
PY - 2026
DA - 2026/06/11
VL - 17
SP - 104005
SN - 2215-0161
PB - Elsevier
DO - 10.1016/j.mex.2026.104005
UR - https://doi.org/10.1016/j.mex.2026.104005
LA - en
ER -

CSL-JSON

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