ArticleScientific reports2023
Correlation between electroencephalographic markers in the healthy brain.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A time-scale representation of EEG Higuchi fractal dimension.Scientific reports · 2026Article
- Neurophysiological correlates to the human brain complexity through q-statistical analysis of electroencephalogram.Scientific reports · 2025Article
- Individual stability of single-channel EEG measures over one year in healthy adults.Scientific reports · 2025Article
- CRoP: Context-wise Robust Static Human-Sensing Personalization.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- Cognitive impairment in chronic kidney disease: role of brain imaging, functional imaging, electroencephalography, cerebrospinal fluid biomarkers and sensors.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2025Review
- VR Cognitive-based Intervention for Enhancing Cognitive Functions and Well-being in Older Adults with Mild Cognitive Impairment: Behavioral and EEG Evidence.Psychosocial intervention · 2025Article
- The Impact of the Natural Level of Blood Biochemicals on Electroencephalographic Markers in Healthy People.Sensors (Basel, Switzerland) · 2024Article
- A novel approach for migraine detection using localized component filtering and electroencephalographic spectral asymmetry index.Current journal of neurology · 2024Article
- A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.PloS one · 2024Article
- Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mental disorders have an increasing tendency and represent the main burden of disease to society today. A wide variety of electroencephalographic (EEG) markers have been successfully used to assess different symptoms of mental disorders. Different EEG markers have demonstrated similar classification accuracy, raising a question of their independence. The current study is aimed to investigate the hypotheses that different EEG markers reveal partly the same EEG features reflecting brain functioning and therefore provide overlapping information. The assessment of the correlations between EEG signal frequency band power, dynamics, and functional connectivity markers demonstrates that a statistically significant correlation is evident in 37 of 66 (56%) comparisons performed between 12 markers of different natures. A significant correlation between the majority of the markers supports the similarity of information in the markers. The results of the performed study confirm the hypotheses that different EEG markers reflect partly the same features in brain functioning. Higuchi's fractal dimension has demonstrated a significant correlation with the 82% of other markers and is suggested to reveal a wide spectrum of various brain disorders. This marker is preferable in the early detection of symptoms of mental disorders.
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