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, Skopje - Republic of Macedonia 1000
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


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 2011;2(1):WMC001493
doi: 10.9754/journal.wmc.2011.001493
Click here
Submitted on: 29 Jan 2011 09:02:32 AM GMT
Published on: 30 Jan 2011 04:09:32 PM GMT


Background: The published results about possible mental effects of mobile phone (MP) 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: MP exposure leads to pronounced asymmetries and individualities in brain rate values, characterizing EEG spectral shifts towards overarousal or underarousal.
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

This study is restricted to mental activity, which is characterized by arousal, defined by the nobelist Kahnemann as the “general activation of the mind” [7]. 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 below one hundred Hz. Moreover, it appeared that the states of arousal are somehow proportional to EEG activity represented by different frequency bands: from delta (0.5-4 Hz) till beta (14-30 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 beta to alert state and anxiety [8].
It is important, and yet often overlooked, that ELF frequencies of the same range are also emitted by MPs of different technologies. For instance, the TDMA (Time Division Multiple Access) technique produces pulse modulation at 8.34 Hz, while in the case of TETRA (Terrestial Enhanced Trunk Radio) technology the transmission is pulsed at 17.6 Hz [1, 9]. In addition, GSM (Global System for Mobile Communication) emission may be characterized by 2 Hz or 4.25 Hz, depending on transmission mode [9]. More specifically, various spectral components in the MP ELF appear to correspond to different modes: 8 Hz – talk; 2, 8 Hz – listen; 1-32 Hz – standby [10, 11].
Consequently, an influence of MP on EEG and mental activity is quite feasible and could be diverse [9, 12].


Analysing the empirical input-output activation, arousal can be represented as a sigmoid function [13]. 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 [14]. Combining these theoretical results with the mentioned empirical data, a summarized arousal-frequency correlation is obtained [15].
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).
Defined as the mean frequency of brain oscillations weighted over the all bands of the EEG potential (or power) spectrum, the brain rate (fb) can be calculated by Eq.1, with Eq.2, where the index i denotes the frequency band (for delta i = 1, for theta i = 2, etc.) and Vi is the corresponding mean amplitude of the electric potential (or power).




The brain rate values for different mental states (sleep stages and some mental disorders), showing underarousal or overarousal, are displayed in Illustration1.
The distribution of mean brain rate values (for 40 healthy adults, eyes closed ), indicating the changes in different cortical regions (Frontal/Back, Left/Right and midline) are shown in Illustration2.
By definition, brain rate is a quantitative measure of EEG spectrum shift and thus can also serve as an indicator of MP effects on brain electrical activity. In what follows, summarizing some published results, pronounced asymmetries and individualities of MP exposure effects on human EEG, are deduced, leading to corresponding variations of brain rate.
So, in a recent double-blind study using Helmholtz coils with six stimulation frequencies from 4 to 50 Hz, significant brain-region dependent changes in alpha and beta bends have been obtained [16]; in terms of brain rate, this means the corresponding increase in frontal region and decrease in back region. Some other investigations on effects of ELF (1.5, 2, 10 or 40 Hz) electromagnetic fields on the intrinsic electrical activity on the human brain [17-20] have shown that the EEG time variability and/of spectral power density have changed individually, both in intensity and in laterality; this leads to correspondingly volatile brain rate. The EEG spectrum effects of GSM MP on event-related synchronization/desynchronization during cognitive processing [21] imply that in both active MP cases (encoding and recognition) the brain rate diminishes.


External ELF fields emitted by MPs can have subtle non-termal effects, including the perturbation of endogenous electromagnetic activity in the brain, thus influencing mental activity and EEG.
Actually, a number of recent comprehensive articles on humans have confirmed this kind of MP effects [e.g. 3, 9, 22]. Thereby, variable results have been obtained, with strong interindividual differences. In particular, the reaction time appeared to be shorter or with no change, the accuracy – poorer or no change, the memory – worse or not replicable, while the sleep – delayed.
In addition, the introduced brain rate, indicating the EEG mental activity changes, could be employed as a complementary biofeedback training parameter, characterizing the whole EEG spectrum (as distinct from e.g. theta-beta ratio). The rationale is that, in practice, whenever a certain band is trained, the other bands are affected too; it may even appear that e.g. “…the changes that occurred as a result of stimulating in the alpha frequency were not in alpha but were in beta…” [23]. This effect may also partially explain the volatility of MP influences on EEG and 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 transcranial magnetic stimulation or of 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 on mental activity.


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 100kHz). 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. Kahnemann A. Attention and Effort. Prentice-Hall, New Jersey, 1973.
8. Pritchard TC, Alloway KD. Medical neuroscience. Madison, CT: Frence Greek Publishing (1999).
9. Hyland GJ. Physical basis of adverse and therapeutic effects of low intensity microwave radiation. Indian Journal of Experimental Biology 2008; 46: 403-419.
10. Hung CS, Anderson C, Horne JA, McEvoy P. Mobile phone ‘talk-mode’ signal delays EEG-determined sleep onset. Neuroscience Letters 2007; 421: 82–86.
11. 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.
12. Pop-Jordanova N. EEG spectra in pediatric research and practice. Prilozi 2008; 29(1): 221-237.
13. Kropotov Y. Quantitative EEG, event-related potentials and neurotherapy. Amsterdam: Elsevier, 2009.
14. Pop Jordanov J, Pop-Jordanova N. Neurophysical substrates of arousal and attention. Cognitive Processing 2009; 10(Suppl. 1): S71–S79.
15. Pop-Jordanov J, Pop-Jordanova N. Quantum transition probabilities and the level of consciousness. Journal of Psychophysiology 2010; 24(2): 136-140.
16. 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.
17. Bell G, Marino A, Chesson A. Frequency-specific responses in the human brain caused by electromagnetic fields. Journal of the Neurological Sciences 1994; 123: 26-32.
18. 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.
19. Klonowski W. From conformons to human brains: an informal overview of nonlinear dynamics and its applications in biomedicine. Nonlinear Biomedical Physics 2007; 1: 5.
20. 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.
21. 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.
22. Philips A and Philips J. Mobile Phones. EMFields 2010.
23. Lubar JF. Neurocortical dynamics: Implications for understanding the role of neurofeedback and related techniques for the enhancement of attention. Applied Psychophysiology and Biofeedback 1997; 22 (2): 111–126.
24. 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.
25. Pop-Jordanova N, Zorcec T, Demerdzieva A, Gucev Z. QEEG characteristics and spectrum weighted frequency for children diagnosed as autistic spectrum disorder. Nonlinear Biomedical Physics 2010; 4: 4.

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.

9 reviews posted so far

The authors thank for the review, including the useful proposals for additional comparisons and possible enhancement. ... View more
Responded by Prof. Jordan Pop-Jordanov on 18 Feb 2011 02:42:42 PM GMT

Non-thermal Effects of EMFs
Posted by Prof. Wlodzimierz Klonowski on 17 Feb 2011 03:21:05 PM GMT

The authors thank to the reviewer and agree with his suggestion concerning the future research. ... View more
Responded by Prof. Jordan Pop-Jordanov on 18 Feb 2011 02:40:59 PM GMT

Posted by Dr. Rash B Dubey on 04 Feb 2011 12:40:06 PM GMT

The authors thank for the reviewer's comments. ... View more
Responded by Prof. Jordan Pop-Jordanov on 18 Feb 2011 02:44:06 PM GMT

Effects of mobile phones on EEG
Posted by Prof. Yuri Kropotov on 31 Jan 2011 03:57:21 PM GMT

The authors are thankfull for the rewiew.... View more
Responded by Prof. Jordan Pop-Jordanov on 04 Feb 2011 12:30:16 PM GMT

The authors are thankful for the review.... View more
Responded by Prof. Jordan Pop-Jordanov on 31 Jan 2011 11:05:44 AM GMT

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

0 comments posted so far

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)