Correlation of EEG Envelopes is the Best Method for Identifying Mental Diseases, Functional States, Individual and Intergroup Differences

Alexey Pavlovich Kulaichev
Alexey Pavlovich Kulaichev

Send Message

To: Author

Correlation of EEG Envelopes is the Best Method for Identifying Mental Diseases, Functional States, Individual and Intergroup Differences

Article Fingerprint

ReserarchID

38NWP

Correlation of EEG Envelopes is the Best Method for Identifying Mental Diseases, Functional States, Individual and Intergroup Differences Banner

AI TAKEAWAY

Connecting with the Eternal Ground
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu
Font Type
Font Size
Font Size
Bedground

Abstract

The principal errors of spectral and coherent analysis are discussed, and the mathematics of these methods is not related to EEG nature. In this regard, in 2011, the new method was developed for evaluating EEG synchrony by the correlation of envelopes, which has a direct and fundamental physiological meaning. The basics of this method and the methodology of subsequent multilateral statistical analysis are considered. The effective use of the method for identifying individual and intergroup differences in the norm and several types of schizophrenia, depressive diseases, five stages of sleep, and similar functional states are presented.

References

22 Cites in Article
  1. A Kulaichev (2018). Actual problems of the humanities and natural sciences.
  2. S Tong,N Thankor (2009). Quantitative EEG for Brain–Computer Interfaces.
  3. A Kulaichev (2016). Inaccuracy of Estimates of Mean EEG Amplitude in Frequency Domains Based on Amplitude and Power Spectrum.
  4. Norbert Wiener (1930). Generalized harmonic analysis.
  5. R Randall (1989). Frequency Analysis.
  6. Donald Walter (1963). Spectral analysis for electroencephalograms: Mathematical determination of neurophysiological relationships from records of limited duration.
  7. A Kulaichev (2011). The Informativeness of Coherence Analysis in EEG Studies.
  8. A Kulaichev,Natalia Gorbachevskaya (2011). Investigating the Differentiation of Norm and Disorders of Schizophrenic Spectrum by Analysis of EEG Correlation Synchrony.
  9. Alexey Kulaichev,Natalia Gorbachevskaya (2013). Differentiation of norm and disorders of schizophrenic spectrum by analysis of EEG correlation synchrony.
  10. Ekaterina Iznak,Andrey Iznak (2014). ЭЭГ-КОРРЕЛЯТЫ НАРУШЕНИЯ ПРИНЯТИЯ ЛОГИЧЕСКИХ РЕШЕНИЙ ПРИ ДЕПРЕССИВНЫХ РАССТРОЙСТВАХ.
  11. A Kulaichev,Natalia Gorbachevskaya (2012). Investigating the Differentiation of Norm and Disorders of Schizophrenic Spectrum by Analysis of EEG Correlation Synchrony.
  12. S Borisov,A Kaplan,N Gorbachevskaya,I Kozlova (2005). Analysis of EEG Structural Synchrony in Adolescents with Schizophrenic Disorders.
  13. M Sterman,David Kaiser (2001). Comodulation.
  14. Reza Boostani,Khadijeh Sadatnezhad,Malihe Sabeti (2009). An efficient classifier to diagnose of schizophrenia based on the EEG signals.
  15. Nuri Ince,Massoud Stephane,Ahmed Tewfik,Giuseppe Pellizzer,Kate Mcclannahan (2007). Schizophrenia Classification using Working Memory MEG ERD/ERS Patterns.
  16. U Sakoglu,A Michael,V Calhoun (2009). Classification of schizophrenia patients vs healthy controls with dynamic functional network connectivity.
  17. G Winterer,M Ziller,H Dorn,K Frick,C Mulert,Y Wuebben,W Herrmann (2000). Frontal dysfunction in schizophrenia - a new electrophysiological classifier for research and clinical applications.
  18. Verner Knott,Colleen Mahoney,Sidney Kennedy,Kenneth Evans (2001). EEG power, frequency, asymmetry and coherence in male depression.
  19. Jürgen Fell,Joachim Röschke,Klaus Mann,Cornelius Schäffner (1996). Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures.
  20. Park Hae-Jeong,Oh Jung-Su,Jeong Do-Un,Park Kwang-Suk (2000). Automated sleep stage scoring using hybrid rule and case-based reasoning.
  21. K Susmakova,A Krakovska (2009). Selection of measures for sleep stages classification.
  22. K Susmakova,A Krakovska (2008). Discrimination ability of individual measures used in sleep stages classification.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Alexey Pavlovich Kulaichev. 2021. \u201cCorrelation of EEG Envelopes is the Best Method for Identifying Mental Diseases, Functional States, Individual and Intergroup Differences\u201d. Global Journal of Medical Research - A: Neurology & Nervous System GJMR-A Volume 21 (GJMR Volume 21 Issue A3).

Download Citation

EEG research: importance in medical research, neurology, and brain studies. Explore EEG methodologies in academic research.
Journal Specifications

Crossref Journal DOI 10.17406/gjmra

Print ISSN 0975-5888

e-ISSN 2249-4618

Keywords
Classification
GJMR-A Classification NLMC Code: WL 141
Version of record

v1.2

Issue date
July 31, 2021

Language
en
Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

Article Matrices
Total Views: 2194
Total Downloads: 947
2026 Trends
Related Research
Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Correlation of EEG Envelopes is the Best Method for Identifying Mental Diseases, Functional States, Individual and Intergroup Differences

Alexey Pavlovich Kulaichev
Alexey Pavlovich Kulaichev

Research Journals