Research articles

By Prof. Jordan Pop-Jordanov , Prof. Nada Pop-Jordanova
Corresponding Author Prof. Nada Pop-Jordanova
Pediatric Clinic, Faculty of Medicine, University of Skopje, Vodnjanska 17 - Republic of Macedonia 1000 Skopje
Submitting Author Prof. Jordan Pop-Jordanov
Other Authors Prof. Jordan Pop-Jordanov
Research Center for Energy, Informatic and Materials of the Macedonian Academy of Sciences and Arts, Krste Misirkov 2 - Republic of Macedonia 1000 Skopje


EEG, mental activity, arousal, brain rate, ELF, mobile phones

Pop-Jordanov J, Pop-Jordanova N. Mobile Phones, E E G And Mental Activity. WebmedCentral BRAIN 2010;1(12):WMC001370
doi: 10.9754/journal.wmc.2010.001370
Submitted on: 27 Dec 2010 10:41:48 AM GMT
Published on: 28 Dec 2010 12:52:33 PM GMT


Background: The published results about possible mental effects of mobile phone exposure are still inconsistent and inconclusive.

Methods: The sigmoid arousal-frequency correlation combined with the brain rate concept is used to characterize the effects of mobile phones on mental states.

Results: Pronounced asymmetries and individualities in brain rate values, characterizing EEG spectral shifts towards overarousal or underarousal, have been obtained.

Conclusion: The mental consequences of mobile phone use could be, in principle, detrimental or beneficial, depending on the individual initial EEG spectra and the different exposure frequencies from mobile phone technologies. Thereby, brain rate can serve as a useful preliminary indicator.


Worldwide concern about possible mental effects of mobile phone (MP) exposure lasts for more than two decades [1-2]. However, the published results are still inconsistent and inconclusive [3-5]. So, in two recent documents from competent organizations (World Health Organization - WHO and International Commission on Non-Ionizing Radiation Protection - ICNIRP) it is stated, respectively: “To date, results of epidemiological studies provide no consistent evidence of a causal relationship between radiofrequency exposure and any adverse health effect. Yet, these studies have too many limitations to completely rule out an association”[4]; and: ”The evidence for neurobehavioral effects on brain electrical activity, cognition, sleep and mood in volunteers exposed to low frequency electric and magnetic fields is much less clear”[5].

 The aim of this article is to present a possible explanation for the ambiguous and inconsistent evidence about MP effects on mental activity. In addition, the applicability of brain rate concept [6] for indicating MP influences is considered.

Empirical evidence

Starting key question is: which EEG frequencies are relevant for mental activity? The well established empirical results show that these are frequencies in the extremely low (ELF) range of bellow one hundred Hz. Moreover, it appeared that the states of arousal are somehow proportional to the EEG activity represented by different frequency bands: from delta (0.5-4 Hz) till gamma (30-50 Hz). Thereby, the empirical data have shown that the delta band corresponds to deep sleep stages 3-4, theta to drowsiness and sleep stages 1-2, alpha to relaxed state, while SMR, beta and gamma to alert state, anxiety and peak performance, respectively [7, 8].

Simultaneously, ELF frequencies of the same range are also emitted by MPs of different technologies (Illustration1). Consequently, an influence of MP on EEG and mental activity is quite feasible and could be diverse [9, 10]. Actually, a number of studies on animals (Illustration2) and humans (Illustration3) has confirmed this kind of MP effects.


Analysing the empirical input-output activation, arousal can be represented as a sigmoid function [11]. An analytical expression of the same sigmoid dependence has been derived by a quantum theoretical approach, based on the transition probabilities from the interaction of brain electric field with neuronal dipoles [12]. Combining these theoretical results with the mentioned empirical data, a summarized arousal-frequency correlation can be represented (Illustration4) [13].

However, the actual electric field in the brain (both endogenous and externally modulated) is not monochromatic, but spectral, characterized by a time-changing frequency distribution. Consequently, a spectrum weighted frequency (brain rate) parameter was introduced [6], as a useful general indicator of mental state (in parallel with temperature, blood pressure or hearth rate, indicating different bodily states) [Illustration5].


The brain rate values for different mental states (sleep stages and some mental disorders), showing underarousal or overarousal, are displayed in Illustration6.

 The distribution of mean brain rate values (for 40 healthy adults), indicating the changes in different cortical regions (Frontal/Back, Left/Right and midline) are shown in Illustration7.

 Summarizing the published results, illustrations 8-10 show pronounced asymmetries and individualities of MP exposure effects on human EEG, which lead to corresponding variations of brain rate as indicator of mental activity.


The inconsistent and inconclusive evidence about MP mental effects could be due to subtle interplay of spectral individualities and radiation specifics, representing neurophysical substrate of mental processes. Having in mind pronounced individual differences concerning EMF effects, it is suggested to refine accordingly the sampling procedure, differentiating specific subgroups for studying.

 Actually, the mental consequences of MP could be detrimental or beneficial, depending on the individual initial EEG spectra and the different exposure frequencies from MP technologies. Moreover, MP use can be considered as a sort of brain gymnastics or neurofeedback training (but still random and not “knowledge based”).

 Thereby, EEG spectrum weighted frequency (brain rate), characterizing the level of mental arousal, can serve as a useful preliminary indicator of possible MP influences, and a training parameter.


The study was partly supported by EU/ESF COST Action BM0704: Emerging EMF Technologies and Health Risk Management.

Authors contributions

JPJ contributed to neurophysical modeling.
NPJ contributed to neuromedical studies.
Both authors participated in the design and approved the final manuscript.


1. Independent Expert Group on Mobile Phones (IEGMP). Mobile phones and health. (Chairman Sir William Stewart). Chilton, UK: NRPB, 2000.
2. Hyland GJ. Physics and biology of mobile telephony. The Lancet 2000; 356:1833-6.
3. Rongen E van, Croft R, Juutilainen J, Lagroye I, Miyakoshi J, Saunders R, de Seze R, Tenforde T, Verschaeve L, Veyret B, Xu Z. Effects of Radiofrequency Electromagnetic Fields on the Human Nervous System. Journal of Toxicology and Environmental Health, Part B, 2009; 12: 572 — 597.
4. WHO. Electromagnetic fields and public health: mobile phones. Fact sheet No 193, May 2010.
5. ICNIRP. Guidelines for limiting exposure to time-varying electric and magnetic fields (1Hz to 100 kHz). Health Physics 2010; 99(6): 816-836.
6. Pop-Jordanova N, Pop-Jordanov J. Spectrum-weighted EEG frequency (“brain rate”) as a quantitative indicator of mental arousal. Prilozi 2005. 26(2): 35–42.
7. Pritchard TC, Alloway KD. Medical neuroscience. Madison, CT: Frence Greek Publishing (1999).
8. Bendorfer K. Alpha-theta neurofeedback: Its promises & challenges. BFE 5th Annual Meeting, Prien, February 2001.
9. Pop-Jordanova N. EEG spectra in pediatric research and practice. Prilozi 2008. 29(1): 221-237.
10. Hyland GJ. Physical basis of adverse and therapeutic effects of low intensity microwave radiation. Indian Journal of Experimental Biology 2008; 46: 403-419.
11. Kropotov Y. Quantitative EEG, event-related potentials and neurotherapy. Amsterdam: Elsevier, 2009.
12. Pop Jordanov J, Pop-Jordanova N. Neurophysical substrates of arousal and attention. Cognitive Processing 2009; 10(Suppl. 1): S71–S79.
13. Pop-Jordanov J, Pop-Jordanova N. Quantum transition probabilities and the level of consciousness. Journal of Psychophysiology 2010; 24(2): 136-140.
14. Hung CS, Anderson C, Horne JA, McEvoy P. Mobile phone ‘talk-mode’ signal delays EEG-determined sleep onset. Neuroscience Letters 2007; 421: 82–86.
15. Gjoneska B, Pop-Jordanova N, Grcev l. Comparative Analysis of Studies Investigating the Influence of Extremely Low Frequency Electromagnetic Fields on Human EEG Patterns. Neuromath Workshop 2007; 18: 51-56.
16. Bawin SM, Gavalas-Medici RJ and Adey WR. Effects of modulated very high frequency fields on specific brain rhythms in cats. Brain Res 1973; 58: 365.
17. Bawin SM, Gavalas-Medici RJ and Adey WR. Reinforcement of transient brain rhitms by amplitude-modulated VHF fileds. In: Biological anad Clinical Effects of Low Frequency Magnetic and Electric Fields (JG Llaurado, A Sances and JH Battocletti, Eds).  Springfield, Charles C Thomas, 1974, 172.
18. Shandala MG, Dumanskii UD, Rudnev MI, Ershova LK and Los IP. Study of nonionizing microwave radiation effects upon the central nervous system and behavior reactions. Environ Health Perspect 1979; 30: 115.
19. McRee DI, Elder JA, Gage MI, Reiter LW Rosenstein LS, Shore ML, Galloway WD, Adey WR and Guy AW. Effects of nonionizing radiation on the central nervous system, behavior and blood: a progress report. Environ Health Perspect 1979; 30: 123.
20. Mitchel CL, MCRee DI, Peterson NJ, Tilson HA, Shandala MG, Rudnev MI, Varetskii VV and Navakatukyan MI. Results of a United States and Soviet Union joint project on nervous system effects of microwave radiation. Environ Health Perspect 1989; 81: 201.
21. Thuroczy G, Kubinyi G, Bodo M, Bakos J and Szabo LD. Simultaneous response of brain electrical activity 9EEG) and cerebral circulation (REG) to microwave exposure in rats. Rev Environ Health 1994; 10: 135.
22. Philips A and Philips J. Mobile Phones. EMFields 2010.
23. Kaniusas E, Varoneckas G, Alonderis A, Podlipskyte A. Hearth rate variability and EEG during sleep using spectrum-weighted frequencies – a case study. COST B27, Symposium Electrical brain oscillation along the life-span, Goetingen October 2007.
24. Pop-Jordanova N, Zorcec T, Demerdzieva A, Gucev Z. WEEG characteristics and spectrum weighted frequency for children diagnosed as autistic spectrum disorder. Nonlinear Biomedical Physics 2010; 4: 4.
25. Cvetkovic D, Jovanov E, Cosic I. Alterations in human EEG activity caused by extremely low frequency electromagnetic fields. In proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA, 2006, 3206-3209.
26. Bell G, Marino A, Chesson A. Frequency-specific responses in the human brain caused by electromagnetic fields. Journal of the Neurological Sciences, (123): 26-32, (1994).
27. Verschueren S, Wieser HG, Dobson J. Preliminary analysis of the effects of DTX mobile phone emissions on the human EEG. In proceedings of the 3rd International Workshop on the Biological Effects of EMFs, Kos, Greece, 2004, 704-712.
28. Klonowski W. From conformons to human brains: an informal overview of nonlinear dynamics and its applications in biomedicine. Nonlinear Biomedical Physics 2007; 1: 5.
29. Hinrikus H, Bachmann M, Kalda J, Sakki M, Lass J and Tomson R. Methods of electroencephalographic signal analysis for detection of small hidden changes. Nonlinear Biomedical Physics 2007; 1: 9.
30. Krause CM, Bjornberg CH, Pesonnen M, Hulten A, Liesivuory, Koivisto M, Revonsuo A, Laine M, Hamalainen H. Mobile phone effects on children’s event-related oscillatory EEG during an auditory memory task. Int. J. Radiat. Biol.2006; 82(6): 443-450.

Source(s) of Funding


Competing Interests



This article has been downloaded from WebmedCentral. With our unique author driven post publication peer review, contents posted on this web portal do not undergo any prepublication peer or editorial review. It is completely the responsibility of the authors to ensure not only scientific and ethical standards of the manuscript but also its grammatical accuracy. Authors must ensure that they obtain all the necessary permissions before submitting any information that requires obtaining a consent or approval from a third party. Authors should also ensure not to submit any information which they do not have the copyright of or of which they have transferred the copyrights to a third party.
Contents on WebmedCentral are purely for biomedical researchers and scientists. They are not meant to cater to the needs of an individual patient. The web portal or any content(s) therein is neither designed to support, nor replace, the relationship that exists between a patient/site visitor and his/her physician. Your use of the WebmedCentral site and its contents is entirely at your own risk. We do not take any responsibility for any harm that you may suffer or inflict on a third person by following the contents of this website.

4 reviews posted so far

The authors are thankful for your review and comments. In the revised manuscript , published on 30 January 2011, (1) the sections of results and discussion are expanded and (2) the information from il... View more
Responded by Prof. Jordan Pop-Jordanov on 31 Jan 2011 10:56:19 AM GMT

Mobile Phones, E E G And Mental Activity
Posted by Prof. Giedrius Varoneckas on 04 Jan 2011 09:16:28 AM GMT

The authors are thankful for your valuable comments and suggestions. In the revised manuscript, published on 30 January 2011, all of them are taken into account, namely: (1) the sections of results an... View more
Responded by Prof. Jordan Pop-Jordanov on 31 Jan 2011 10:56:52 AM GMT

The authors are grateful for your valuable review and comments. Concerning the tables and illustrations, in the revised paper, published on 30 January 2011, their quality is improved and the number i... View more
Responded by Prof. Jordan Pop-Jordanov on 31 Jan 2011 10:59:22 AM GMT

Mobile Phones and Brain
Posted by Prof. Nataliya A Babenko on 29 Dec 2010 03:13:00 PM GMT

The authors are thankful for your valuable comments and suggestions. In the revised paper, published on 30 January 2011, more attention is paid to the discussion, the quality of illustrations is impro... View more
Responded by Prof. Jordan Pop-Jordanov on 31 Jan 2011 11:00:07 AM GMT

1 comment posted so far

Untitled Posted by Dr. Riccardo R Meucci on 24 Feb 2011 10:33:32 AM GMT

Please use this functionality to flag objectionable, inappropriate, inaccurate, and offensive content to WebmedCentral Team and the authors.


Author Comments
0 comments posted so far


What is article Popularity?

Article popularity is calculated by considering the scores: age of the article
Popularity = (P - 1) / (T + 2)^1.5
P : points is the sum of individual scores, which includes article Views, Downloads, Reviews, Comments and their weightage

Scores   Weightage
Views Points X 1
Download Points X 2
Comment Points X 5
Review Points X 10
Points= sum(Views Points + Download Points + Comment Points + Review Points)
T : time since submission in hours.
P is subtracted by 1 to negate submitter's vote.
Age factor is (time since submission in hours plus two) to the power of 1.5.factor.

How Article Quality Works?

For each article Authors/Readers, Reviewers and WMC Editors can review/rate the articles. These ratings are used to determine Feedback Scores.

In most cases, article receive ratings in the range of 0 to 10. We calculate average of all the ratings and consider it as article quality.

Quality=Average(Authors/Readers Ratings + Reviewers Ratings + WMC Editor Ratings)