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Stepan V. Buikin
Kostroma State University
Dmitry B. Vorontsov
Kostroma State University
Tatiana N. Adeeva
Kostroma State University
Vladimir A. Konyshev
Neurobotics Company
Dmitry Vl. Konyshev
Neurobotics Company
Svetlana S. Golitsina
Kostroma State University
Nina G. Shishova
Kostroma State University
Neurophysiological markers of the duration of focused observation
Buikin S.V., Vorontsov D.B., Adeeva T.N., Golitsina S.S., Shishova Ni.G. Neurophysiological markers of the duration of focused observation Vestnik of Kostroma State University. Series: Pedagogy. Psychology. Sociokinetics, 2026, vol. 32, no. 1, pp. 134-143. https://doi.org/10.34216/2073-1426-2026-32-1-134-143
DOI: https://doi.org/10.34216/2073-1426-2026-32-1-134-143
УДК: 159.93
EDN: QISDOQ
Publish date: 2025-12-24
Annotation: The duration of focused attention is a critical cognitive process that determines the effectiveness of an activity. The present study is aimed at identifying neurophysiological markers of sustained attention and their relationship to the type of cognitive load. The study involved 155 healthy volunteers aged 18 to 65 years (average age 32.4 ± 12.8 years, 78 women, 77 men) who performed two cognitive tasks of different modalities: the visual Landolt test (15 minutes) and the auditory correction test (6 minutes). At the same time, an electroencephalogram was recorded using a 6-channel NeuroPlay-6C system with Fp1, Fp2, T3, T4, O1, O2 electrodes placed according to the international 10-20 system. Analysis of the spectral characteristics of the EEG revealed that the duration of focused observation is directly related to the dynamics of the power of theta, alpha and beta rhythms in various cortical leads. It was found that when performing a visual task, there is an increase in theta rhythm power in the occipital regions (O1, O2), accompanied by a decrease in beta activity in the temporal leads (T3, T4), which correlates with an increase in reaction time and a decrease in accuracy. An increase in the frontal theta rhythm (Fp1, Fp2) and a change in alpha activity in the parietotemporal regions were detected during auditory loading.
Keywords: electroencephalography, cognitive load, long-term focused observation, focused attention, Landolt test, auditory test, neuromarkers.
Literature list: Bazanova O.M. Sovremennaya interpretaciya al`fa-aktivnosti e`lektroe`ncefalogrammy` [Modern interpretation of alpha activity of the electroencephalogram]. Uspexi fiziologicheskix nauk [Advances in Physiological Sciences], 2009, vol. 40, no. 3, pp. 32–53. (In Russ.) Anguera J.A., Boccanfuso J., Rintoul J.L., Al-Hashimi O. et al. Video game training enhances cognitive control in older adults. Nature, 2013, no. 501 (7465), pp. 97–101. Antonenko P., Paas F., Grabner R., Van Gog T. Using electroencephalography to measure cognitive load. Educational Psychology Review, 2010, vol. 22 (4), pp. 425–438. Barry R.J., Clarke A.R., Johnstone S.J., Magee C.A. et al. EEG differences between eyes-closed and eyes-open resting conditions. Clinical Neurophysiology, 2007, vol. 118 (12), pp. 2765–2773. Boksem M.A., Meijman T.F., Lorist M.M. Effects of mental fatigue on attention: An ERP study. Cognitive Brain Research, 2005, vol. 25 (1), pp. 107–116. Borghini G., Astolfi L., Vecchiato G., Mattia D. et al. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neuroscience & Biobehavioral Reviews, 2014, vol. 44, pp. 58–75. Buffalo E.A., Fries P., Landman R., Buschman T.J. et al. Laminar differences in gamma and alpha coherence in the ventral stream. Proceedings of the National Academy of Sciences, 2011, vol. 108 (27), pp. 11262–11267. Chiang A.K., Rennie C.J., Robinson P.A., van Albada S.J. et al. Age trends and sex differences of alpha rhythms including split alpha peaks. Clinical Neurophysiology, 2011, vol. 122 (8), pp. 1505–1517. Gevins A., Smith M.E. Neurophysiological measures of cognitive workload during human-computer interaction. Theoretical Issues in Ergonomics Science, 2003, vol. 4 (1-2), pp. 113–131. Gola M., Magnuski M., Szumska I., Wróbel A. EEG beta band activity is related to attention and attentional deficits in the visual performance of elderly subjects. International Journal of Psychophysiology, 2013, vol. 89 (3), pp. 334–341. Grandy T.H., Werkle-Bergner M., Chicherio C., Lövdén M. et al. Individual alpha peak frequency is related to latent factors of general cognitive abilities. NeuroImage, 2013, vol. 79, pp. 10–18. Jap B.T., Lal S., Fischer P., Bekiaris E. Using EEG spectral components to assess algorithms for detecting fatigue. Expert Systems with Applications, 2009, vol. 36 (2), pp. 2352–2359. Jensen O., Tesche C.D. Frontal theta activity in humans increases with memory load in a working memory task. European Journal of Neuroscience, 2002, vol. 15 (8), pp. 1395–1399. Kamzanova A.T., Matthews G., Kustubayeva A.M., Jakupov S.M. EEG indices to time-on-task effects and to a workload manipulation (cueing). World Academy of Science, Engineering and Technology, 2014, vol. 8 (2), pp. 1919–1922. Klimesch W. EEG-alpha rhythms and memory processes. International Journal of Psychophysiology, 1997, vol. 26 (1-3), pp. 319–340. Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 1999, vol. 29(2-3), pp. 169–195. Lin C.T., Chuang C.H., Huang C.S. et al. Wireless and wearable EEG system for evaluating driver vigilance. IEEE Transactions on Biomedical Circuits and Systems, 2014, vol. 8 (2), pp. 165–176. MacLean M.H., Arnell K.M., Busseri M.A. Dispositional affect predicts temporal attention costs in the attentional blink paradigm. Cognition & Emotion, 2012, vol. 26 (8), pp. 1549–1564. McIntosh A.R., Grady C.L., Ungerleider L.G., Haxby J.V. et al. Network analysis of cortical visual pathways mapped with PET. Journal of Neuroscience, 1994, vol. 14 (2), pp. 655–666. Pal N.R., Chuang C.Y., Ko L.W., Chao C.F. et al. EEG-based subject-and session-independent drowsiness detection: An unsupervised approach. EURASIP Journal on Advances in Signal Processing, 2008, no. 1, p. 11. Palva J.M., Monto S., Kulashekhar S., Palva S. Neuronal synchrony reveals working memory networks and predicts individual memory capacity. Proceedings of the National Academy of Sciences, 2010, vol. 107 (16), pp. 7580–7585. Pope A.T., Bogart E.H., Bartolome D.S. Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology, 1995, vol. 40 (1-2), pp. 187–195. Portnova G.V., Atanov M.S., Skiteva L.I. Dynamics of EEG during development and aging. Human Physiology, 2022, vol. 48 (6), pp. 630–641. Puma S., Matton N., Paubel P.V., Raufaste É., & El-Yagoubi R. Using theta and alpha band power to assess cognitive workload in multitasking environments. International Journal of Psychophysiology, 2018, vol. 123, pp. 111–120. Rangaswamy M., Porjesz B., Chorlian D.B., Wang K. et al. Beta power in the EEG of alcoholics. Biological Psychiatry, 2002, vol. 52 (8), pp. 831–842. Raufi B., Longo L. An evaluation of the EEG alpha-to-theta and theta-to-alpha band ratios as indexes of mental workload. Frontiers in Neuroinformatics, 2022, no. 16, 861967. Reynolds G.D., Courage M.L., Richards J.E. Infant attention and visual preferences: Converging evidence from behavior, event-related potentials, and cortical source localization. Developmental Psychology, 2010, vol. 46 (4), pp. 886–904. Richards J.E. Attention affects the recognition of briefly presented visual stimuli in infants: An ERP study. Developmental Science, 2003, vol. 6 (3), pp. 312–328. Salthouse T.A. Consequences of age-related cognitive declines. Annual Review of Psychology, 2012, vol. 63, pp. 201–226. Smallwood J., Schooler J.W. The science of mind wandering: Empirically navigating the stream of consciousness. Annual Review of Psychology, 2015, vol. 66, pp. 487–518. Spitzer B., Haegens S. Beyond the status quo: A role for beta oscillations in endogenous content (re) activation. eNeuro, 2017, vol. 4 (4), ENEURO.0170-17.2017. Tsang P.S., Vidulich M.A. Mental workload and situation awareness. Handbook of human factors and ergonomics, ed. by G. Salvendy. John Wiley & Sons, 2006, pp. 243–268. Vysata O., Kukal J., Prochazka A., Pazdera L. et al. Age-related changes in EEG coherence. Neurologia i Neurochirurgia Polska, 2014, vol. 48 (1), pp. 35–38. Woodman G.F., Luck S.J. Electrophysiological measurement of rapid shifts of attention during visual search. Nature, 1999, vol. 400 (6747), pp. 867–869. Wróbel A. Beta activity: A carrier for visual attention. Acta Neurobiologiae Experimentalis, 2000, vol. 60 (2), pp. 247–260. Xie B., Salvendy G. Prediction of mental workload in single and multiple tasks environments. International Journal of Cognitive Ergonomics, 2000, vol. 4 (3), pp. 213–242.
Author's info: Stepan V. Buikin, Candidate of Medical Sciences, Kostroma State University, Kostroma, Russia, bsv@kosgos.ru, https://orcid.org/0000-0002-2648-3245
Co-author's info: Dmitry B. Vorontsov, Candidate of Pedagogical Sciences, Kostroma State University, Kostroma, Russia, d-vorontsov@kosgos.ru, https://orcid.org/0000-0003-1427-0602
Co-author's info: Tatiana N. Adeeva, Candidate of Psychological Sciences, Kostroma State University, Kostroma, Russia, t_adeeva@kosgos.ru, https://orcid.org/0000-0002-0310-7546
Co-author's info: Vladimir A. Konyshev, Chief Executive Officer of Neurobotics Company, v.konyshev@neurobotics.ru, https://orcid.org/0009-0004-0576-2626
Co-author's info: Dmitry Vl. Konyshev, Head of the Development Department at Neurobotics, d.konyshev@neurobotics.ru, https://orcid.org/0000-0002-7078-9989
Co-author's info: Svetlana S. Golitsina, Kostroma State University, Kostroma, Russia, s_golicina@kosgos.ru, https://orcid.org/0000-0002-1160-6345
Co-author's info: Nina G. Shishova, Kostroma State University, Kostroma, Russia, n_shishova@kosgos.ru, https://orcid.org/0000-0002-8648-408X