Harmonized-Multinational qEEG norms (HarMNqEEG)

Min Li, Ying Wang, Carlos Lopez-Naranjo, Shiang Hu, Ronaldo César García Reyes, Deirel Paz-Linares, Ariosky Areces-Gonzalez, Aini Ismafairus Abd Hamid, Alan C. Evans, Alexander N. Savostyanov, Ana Calzada-Reyes, Arno Villringer, Carlos A. Tobon-Quintero, Daysi Garcia-Agustin, Dezhong Yao, Li Dong, Eduardo Aubert-Vazquez, Faruque Reza, Fuleah Abdul Razzaq, Hazim OmarJafri Malin Abdullah, Janina R. Galler, John F. Ochoa-Gomez, Leslie S. Prichep, Lidice Galan-Garcia, Lilia Morales-Chacon, Mitchell J. Valdes-Sosa, Marius Tröndle, Mohd Faizal Mohd Zulkifly, Muhammad Riddha Bin Abdul Rahman, Natalya S. Milakhina, Nicolas Langer, Pavel Rudych, Thomas Koenig, Trinidad A. Virues-Alba, Xu Lei, Maria L. Bringas-Vega, Jorge F. Bosch-Bayard, Pedro Antonio Valdes-Sosa

Research output: Contribution to journalArticlepeer-review


This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG “batch effects” and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

Original languageEnglish
Article number119190
Early online date7 Apr 2022
Publication statusPublished - 1 Aug 2022


  • Batch effects
  • Covid induced brain dysfunction
  • Developmental Brain Chart
  • EEG cross-spectrum
  • Harmonization
  • Malnutrition
  • Quantitative EEG
  • Riemannian geometry
  • Z-score




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