Single neurofeedback session (based on IAF) effect on resting state EEG spectral characteristics and effectiveness of alternative uses task performance

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The study is dedicated to the investigation of EEG spectral characteristics during resting states and a creative task performance (Alternative Uses Task, AUT) before and after a single session of neurofeedback (NFB) and sham-NFB training. The study involved 24 adolescents (aged 15–17 years) who were randomly divided into two independent groups both with 12 subjects. The test group (TEST) participated in one session of NFB training based on their own EEG data (power of individual alpha frequency), while the control group (SHAM) participated in one session of sham-NFB training. Spectral power in the Δ (1.5–4 Hz)-, θ (4–8 Hz)-, α1 (8–10 Hz)-, α2 (10–13 Hz)-, β1 (13–18 Hz)-, β2 (18–30 Hz)-bands of the EEG during eyes open and closed resting states, and event-related synchronisation/desynchronisation of the EEG during performance of the alternative use task before and after the NFB/SHAM session were analysed. Prior to the NFB/SHAM sessions, no differences were observed between the groups in the resting state EEG. After the NFB/SHAM session, lower EEG power values in the β2-band were observed in the test group compared to the control group in the eyes-closed condition. There was a decrease in Δ-band EEG power in frontal temporal regions in the eyes-closed condition and an increase in α2-band power in the eyes-open condition after the NFB session compared to a condition before the NFB session. In the control group, no differences in EEG spectral power were observed in the states AFTER vs. BEFORE the SHAM session. Analysis of event-related EEG synchronisation/desynchronisation during the AUT before and after the NFB session revealed no differences between the test and control groups. Intragroup comparisons of AFTER vs. BEFORE NFB/SHAM sessions revealed the following different effects: in the test group, first, EEG desynchronisation in the frequency range 17.5–30 Hz was observed in the frontal regions of the left hemisphere in the interval 220–300 ms after the presentation of the stimulus, and subsequently, there was synchronisation in the θ and low-frequency α electroencephalogram (EEG) ranges (4–9.8 Hz) (in the interval 540–1400 ms) with maximum differences in the frontal regions. The control group was characterised by synchronisation of electroencephalogram (EEG) activity in the higher frequency ranges of 9.5–26 Hz and in the narrower time interval of 520–760 ms in central and frontal electrodes. Consequently, a single NFB session in the test group resulted in changes in EEG spectral power during resting states that were not observed in the control (SHAM) group following sham training, and exhibited precise modulation of the state during creative activity.

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Sobre autores

A. Grokhotova

Sechenov Institute of Evolutionary Physiology and Biochemistry of the RAS

Autor responsável pela correspondência
Email: anya.annie@yandex.ru
Rússia, St. Petersburg

Zh. Nagornova

Sechenov Institute of Evolutionary Physiology and Biochemistry of the RAS

Email: anya.annie@yandex.ru
Rússia, St. Petersburg

N. Shemyakina

Sechenov Institute of Evolutionary Physiology and Biochemistry of the RAS

Email: anya.annie@yandex.ru
Rússia, St. Petersburg

Bibliografia

  1. Guilford J.P. The structure of intellect // Psychol. Bull. 1956. V. 53. № 4. P. 267.
  2. Guilford J.P., Christensen P.R., Merrifield P.R., Wilson R.C. Alternate uses: Manual of instructions and interpretations. Orange, CA: Sheridan Psychological Services, 1978. 24 p.
  3. Guilford J.P. The nature of human intelligence. McGraw-Hill, 1967. P. 538.
  4. Runco M.A., Jaeger G.J. The standard definition of creativity // Creat. Res. J. 2012. V. 24. № 1. P. 92.
  5. Sternberg R.J., Lubart T.I. The concept of creativity: Prospects and paradigms / Handbook of creativity. Ed. Sternberg R.J. Cambridge: Cambridge University Press, 1999. P. 3.
  6. Bechtereva N.P., Shemyakina N.V., Starchenko M.G. et al. Error detection mechanisms of the brain: Back ground and prospects // Int. J. Psychophysiol. 2005. V. 5. № 2–3. P. 227.
  7. Shemyakina N.V., Danko S.G., Nagornova Zh.V. et al. Changes in the power and coherence spectra of the EEG rhythmic components during solution of a verbal creative task of overcoming a stereotype // Human Physiology. 2007. V. 33. № 5. P. 524.
  8. Camarda A., Salvia É., Vidal J. et al. Neural basis of functional fixedness during creative idea generation: An EEG study // Neuropsychologia. 2018. V. 118. Pt. A. P. 4.
  9. Fink A., Graif B., Neubauer A.C. Brain correlates underlying creative thinking: EEG alpha activity in professional vs. novice dancers // NeuroImage. 2009. V. 46. № 3. P. 854.
  10. Fink A., Schwab D., Papousek I. Sensitivity of EEG upper alpha activity to cognitive and affective creativity interventions // Int. J. Psychophysiol. 2011. V. 82. № 3. P. 233.
  11. Kröger S., Rutter B., Hill H. et al. An ERP study of passive creative conceptual expansion using a modified alternate uses task // Brain Res. 2011. V. 1527. P. 189.
  12. Kraus B., Cadle C., Simon-Dack S. EEG alpha activity is moderated by the serial order effect during divergent thinking // Biol. Psychol. 2019. V. 145. P. 84.
  13. Stevens C.E., Jr., Zabelina D.L. Classifying creativity: Applying machine learning techniques to divergent thinking EEG data // NeuroImage. 2020. V. 219. P. 116990.
  14. Rominger C., Gubler D.A., Makowski L.M., Troche S.J. More creative ideas are associated with increased right posterior power and frontal-parietal/occipital coupling in the upper alpha band: A within-subjects study // Int. J. Psychophysiol. 2022. V. 181. P. 95.
  15. Nagornova Z.V., Galkin V.A., Vasen’kina V.A. et al. Neurophysiological characteristics of alternative uses task performance by means of ERP and ERS/ERD data analysis depending on the subject’s productivity and originality levels // Human Physiology. 2022. V. 48. № 6. P. 609.
  16. Nagornova Z.V., Shemyakina N.V. Influence of cooperation on the event-related potentials in verbal creative and noncreative tasks performance // J. Evol. Biochem. Phys. 2024. V. 60. P. 104.
  17. Mazza A., Dal Monte O., Schintu S. et al. Beyond alpha-band: The neural correlate of creative thinking // Neuropsychologia. 2023. V. 179. P. 108446.
  18. Bartoli E., Devara E., Dang H. Q. et al. Default mode network electrophysiological dynamics and causal role in creative thinking // Brain. 2024. V. 147. № 10. P. 3409.
  19. Fink A., Grabner R.H., Benedek M., Neubauer A.C. Divergent thinking training is related to frontal electroencephalogram alpha synchronization // Eur. J. Neurosci. 2006. V. 23. № 8. P. 2241.
  20. Luft C.D.B., Zioga I., Thompson N.M. et al. Right temporal alpha oscillations as a neural mechanism for inhibiting obvious associations // Proc. Natl. Acad. Sci. U.S.A. 2018. V. 115. № 52. P. E12144.
  21. Mednick S.A., Mednick M.T. Manual, the remote associates test, Form I. Boston, Mass.: Houghton-Mifflin, 1967. P. 32.
  22. Benedek M., Bergner S., Könen T. et al. EEG alpha synchronization is related to top-down processing in convergent and divergent thinking // Neuropsychologia. 2011. V. 49. № 12. P. 3505.
  23. Mölle M., Marshall L., Wolf B. et al. EEG complexity and performance measures of creative thinking // Psychophysiology. 1999. V. 36. № 1. P. 95.
  24. Stark M.B., Vasilevsky N.N. [Biofeedback: Theory and Practice]. Novosibirsk: Nauka, Sibirskoe otdelenie, 1988. 168 p.
  25. Soroko S.I., Trubachev V.V. [Neurophysiological and psychophysiological foundations of adaptive biofeedback]. St. Petersburg: Politekhnika-servis, 2010. 607 p.
  26. Loriette C., Ziane C., Ben Hamed S. Neurofeedback for cognitive enhancement and intervention and brain plasticity // Rev. Neurol. 2021. V. 177. № 9. P. 1133.
  27. Katkin E.S., Fitzgerald C.R., Shapiro D. Clinical applications of biofeedback: Current status and future prospects / Psychology: From research to practice // Eds. Pick H.L., Leibowitz H.W., Singer J.E., Steinschneider A., Stevenson H.W. Boston, MA: Springer, 1978. P. 243.
  28. Rogala J., Jurewicz K., Paluch K. et al. The do's and don'ts of neurofeedback training: A review of the controlled studies using healthy adults // Front. Hum. Neurosci. 2016. V. 10. P. 301.
  29. Enriquez-Geppert S., Huster R.J., Herrmann C.S. EEG-neurofeedback as a tool to modulate cognition and behavior: A review tutorial // Front. Hum. Neurosci. 2017. V. 11. P. 51.
  30. Rahmati N., Rostami R., Zali M.R. et al. The effectiveness of neurofeedback on enhancing cognitive process involved in entrepreneurship abilities among primary school students in district No. 3 Tehran // Basic Clin. Neurosci. 2014. V. 5. № 4. P. 277.
  31. Egner T., Gruzelier J.H. Ecological validity of neurofeedback: Modula tion of slow wave EEG enhances musical performance // Neuroreport. 2003. V. 14. № 9. P. 1225.
  32. Gruzelier J.H. EEG-neurofeedback for optimising performance. II: creativity, the performing arts and ecological validity // Neurosci. Biobehav. Rev. 2014. V. 44. P. 142.
  33. Agnoli S., Zanon M., Mastria S. et al. Enhancing creative cognition with a rapid right-parietal neurofeedback procedure // Neuropsychologia. 2018. V. 118. Pt. A. P. 99.
  34. Boynton T. Applied research using alpha/theta training for enhancing creativity and well-being // J. Neurother. 2001. V. 5. № 1–2. P. 5.
  35. Klimesch W., Schimke H., Pfurtscheller G. Alpha frequency, cognitive load and memory performance // Brain Topogr. 1993. V. 5. № 3. P. 241.
  36. Barry R.J., Clarke A.R., Johnstone S.J. еt al. EEG differences between eyes-closed and eyes-open resting conditions // Clin. Neurophysiol. 2007. V. 118. № 12. P. 2765.
  37. Klimesch W., Sauseng P., Hanslmayr S. EEG alpha oscillations: the inhibition-timing hypothesis // Brain Res. Rev. 2007. V. 53. № 1. P. 63.
  38. Nunez P., Wingeier B., Silberstein R. Spatial-temporal structures of human alpha rhythms: Theory, microcurrent sources, multiscale measurements, and global binding of networks // Hum. Brain Mapp. 2001. V. 13. № 3. P. 125.
  39. Bazanova O.M. [Current interpretation of EEG alpha activity] // Int. Neurol. J. 2011. № 8. P. 96.
  40. Li B.Z., Nan W., Pun S.H. et al. Modulating individual alpha frequency through short-term neurofeedback for cognitive enhancement in healthy young adults // Brain Sci. 2023. V. 13. № 6. P. 926.
  41. Zoefel B., Huster R.J., Herrmann C.S. Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance // NeuroImage. 2011. V. 54. № 2. P. 1427.
  42. Nan W., Rodrigues J.P., Ma J. et al. Individual alpha neurofeedback training effect on short term memory // Int. J. Psychophysiol. 2012. V. 86. № 1. P. 83.
  43. Bobby J.S., Prakash S. Upper alpha neurofeedback training enhances working memory performance using LabVIEW // Int. J. Biomed. Eng. Technol. 2017. V. 25. № 2-4. P. 120.
  44. Enriquez-Geppert S., Huster R.J., Figge C., Herrmann C.S. Self-regulation of frontal-midline theta facilitates memory updating and mental set shifting // Front. Behav. Neurosci. 2014. V. 8. P. 420.
  45. Escolano C., Navarro-Gil M., Garcia-Campayo J., Minguez J. The effects of a single session of upper alpha neurofeedback for cognitive enhancement: A sham-controlled study // Appl. Psychophysiol. Biofeedback. 2014. V. 39. № 3-4. P. 227.
  46. Karvasarskiy B.D. [Clinical Psychology]. Textbook. Saint Petersburg: Piter, 2004. 960 p.
  47. Witkin H.A., Oltman P.K., Raskin E., Karp S.A. A manual for the embedded figures tests. Palo Alto, CA: Consulting Psychologists Press, 1971. P. 15.
  48. Raven J., Raven J. Raven Progressive Matrices / Handbook of nonverbal assessment // Ed. McCallum R.S. Plenum Publishers, 2003. P. 223.
  49. Raven J., Styles I. [Raven's Standard Progressive Matrices Plus (SPM+): Complete Series A-E]. Moscow: Kogito-Tsentr, 2001. 64 p.
  50. Voronin A.N., Galkina T.V. [Diagnostics of verbal creativity (adaptation of Mednick's test)] // Methods of Psychological Diagnostics. 1994. № 2. P. 40.
  51. Tunik E.E. [Creativity Diagnostics: E. Torrance Test]. Adapted Versio. St. Petersburg: Rech, 2006. 176 p.
  52. Kotik M.A. [Psychology and Safety]. Tallinn: Valgus, 1989. 440 p.
  53. Andreeva A.D., Prikhozhan A.M. [Method for diagnosing learning motivation and emotional attitude toward learning in middle and high school students] // Psychological Diagnostics. 2006. № 1. P. 33.
  54. Vigario R.N. Extraction of ocular artefacts from EEG using independent component analysis // Electroencephalogr. Clin. Neurophysiol. 1997. V. 103. № 3. P. 395.
  55. Jung T.P., Makeig S., Westerfield M. et al. Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects // Clin. Neurophysiol. 2000. V. 111. № 10. P. 1745.
  56. Tereshchenko E.P., Ponomarev V.A., Kropotov Yu.D., Müller A. Comparative efficiencies of different methods for removing blink artifacts in analyzing quantitative electroencephalogram and event-related potentials // Human Physiology. 2009. V. 35. № 2. P. 241.
  57. Bendat J.C., Piersol A.G. Random data: Analysis and measurement procedures. 2nd ed. New York, NY, USA: John Wiley & Sons, 1986. P. 592.
  58. Greenhouse S.W., Geisser S. On methods in the analysis of profile data // Psychometrika. 1959. V. 24. P. 95.
  59. Tallon-Baudry C., Bertrand O. Oscillatory gamma activity in humans and its role in object representation // Trends Cogn. Sci. 1999. V. 3. № 4. P. 151.
  60. Maris E., Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data // J. Neurosci. Methods. 2007. V. 164. № 1. P. 177.
  61. Pronina M.V., Ponomarev V.A., Kropotov Yu.D. [Effect of task complexity on the post-movement beta synchronization in the sensorimotor cortex] // J. Sechenov Russ. J. Physiol. 2022. V. 108. № 11. P. 1442.
  62. Nikishena I.S., Ponomarev V.A., Kropotov Yu.D. Event-related potentials of the human brain during the comparison of visual stimuli // Human Physiology. 2023. V. 49. № 3. P. 264.
  63. Alaraj M., Fukami T. Quantitative evaluation for the wakefulness state using complexity-based decision threshold value in EEG signals / 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka, Japan, July 3-7, 2013. P. 6175. doi: 10.1109/EMBC.2013.6610963
  64. Altınkaynak M., Yeşilbaş D., Batbat T. et al. Multimodal analysis of cortical activation in young male adults with internet gaming disorder: A resting state EEG-fNIRS study // J. Psychiatr. Res. 2024. V. 177. P. 368.
  65. Ali A., Afridi R., Soomro T.A. et al. A single-channel wireless EEG headset enabled neural activities analysis for mental healthcare applications // Wirel. Pers. Commun. 2022. V. 125. № 4. P. 3699.
  66. Kim D.K., Rhee J.H., Kang S.W. Reorganization of the brain and heart rhythm during autogenic meditation // Front. Integr. Neurosci. 2024. V. 7. P. 109.
  67. Marzbani H., Marateb H.R., Mansourian M. Neurofeedback: A Comprehensive Review on System Design, Methodology and Clinical Applications // Basic Clin. Neurosci. 2016. V. 7. № 2. P. 143.
  68. Hanslmayr S., Sauseng P., Doppelmayr M. et al. Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects // Appl. Psychophysiol. Biofeedback. 2005. V. 30. № 1. P. 1.
  69. Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis // Brain Res. Brain Res. Rev. 1999. V. 29. № 2–3. P. 169.
  70. Knyazev G.G. EEG delta oscillations as a correlate of basic homeostatic and motivational processes // Neurosci. Biobehav. Rev. 2012. V. 36. № 1. P. 677.
  71. Kiroy V.N., Warsawskaya L.V., Voynov V.B. EEG after prolonged mental activity // Int. J. Neurosci. 1996. V. 85. № 1–2. P. 31.
  72. Lal S.K.L., Craig A. Driver fatigue: Electroencepha-lography and psychological assessment // Psycho-physiology. 2002. V. 39. № 3. P. 313.
  73. Lal S.K.L., Craig A. Reproducibility of the spectral components of the electroencephalogram during driver fatigue // Int. J. Psychophysiol. 2005. V. 55. № 2. P. 137.
  74. Lapomarda G., Valer S., Job R. et al. Built to last: Theta and delta changes in resting-state EEG activity after regulating emotions // Brain Behav. 2022. V. 12. № 6. P. e2598.
  75. Aldemir R., Demirci E., Per H. et al. Investigation of attention deficit hyperactivity disorder (ADHD) sub-types in children via EEG frequency domain analysis // Int. J. Neurosci. 2018. V. 128. № 4. P. 349.
  76. Wang H., Hou Y., Zhan S. et al. EEG biofeedback decreases theta and beta power while increasing alpha power in insomniacs: An open-label study // Brain Sci. 2023. V. 13. № 11. P. 1542.
  77. Ozga W.K., Zapała D., Wierzgała P. et al. Acoustic neurofeedback increases beta ERD during mental rotation task // Appl. Psychophysiol. Biofeedback. 2019. V. 44. № 2. P. 103.
  78. Gilhooly K.J., Fioratou E., Anthony S.H., Wynn V. Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects // Br. J. Psychol. 2007. V. 98. P. 611.
  79. Klimesch W., Schimke H., Schwaiger J. Episodic and semantic memory: an analysis in the EEG theta and alpha band // Electroencephalogr. Clin. Neurophysiol. 1994. V. 91. № 6. P. 428.
  80. Sauseng P., Klimesch W., Schabus M., Doppelmayr M. Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory // Int. J. Psychophysiol. 2005. V. 57. № 2. P. 97.
  81. Knyazev G.G. Motivation, emotion, and their inhibitory control mirrored in brain oscillations // Neurosci. Biobehav. Rev. 2007. V. 31. № 3. P. 377.
  82. Cooper N.R., Burgess A.P., Croft R.J., Gruzelier J.H. Investigating evoked and induced electroencephalo-gram activity in task-related alpha power increases during an internally directed attention task // Neuroreport. 2006. V. 17. № 2. P. 205.
  83. Rusalova M.N., Kostyunina M.B. Frequency and amplitude characteristics of the left and right hemispheres in mental representation of emotionally colored images // Fiziologiia Cheloveka. 1999. V. 25. № 5. P. 50.
  84. Fink A., Benedek M. EEG alpha power and creative ideation // Neurosci. Biobehav. Rev. 2014. V. 44. № 100. P. 111.
  85. Ray W.J., Cole H.W. EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes // Science. 1985. V. 228. № 4700. P. 750.
  86. Cooper N.R., Croft R.J., Dominey S.J. et al. Paradox lost? Exploring the role of alpha oscillations during externally vs. internally directed attention and the implications for idling and inhibition hypotheses // Int. J. Psychophysiol. 2003. V. 47. № 1. P. 65.
  87. Lubar J.F. Discourse on the development of EEG diagnostics and biofeedback for attention-deficit/hyperactivity disorders // Biofeedback Self Regul. 1991. V. 16. № 3. P. 201.
  88. Kamiński J., Brzezicka A., Gola M., Wróbel A. Beta band oscillations engagement in human alertness process. International // Int. J. Psychophysiol. 2012. V. 85. № 1. P. 125.

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2. Fig. 1. Scheme of the physiological part of the study. Fo/Fz – recording of background activity of the electroencephalogram in the state with open/closed eyes; AUT – task for alternative uses (Alternative Uses Task [2]), the numbers 1/2 indicate repetitions of protocols with different stimuli; nBFB/SHAM – session of controlling the power of one’s own α-rhythm/session of controlling someone else’s α-rhythm, “fictitious” training.

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3. Fig. 2. Scheme of the test for the alternative uses test (Alternative Uses Task, AUT). The task had two repetitions (70/80 trials) – one protocol was performed before the nBFB/SHAM session, the other (with other stimuli) – after. After completing the AUT, the subject was asked to assess the sign and degree of expression of the emotions evoked by the task - on a scale from -10 to +10, where "-10" is the most negative emotions, and "+10" is the most positive emotions. The subjects also assessed the difficulty of the task in the range of values (1; 10), where 1 is "very easy" and 10 is "very difficult". The subjects were asked questions about the subjective feeling of the occurrence of the number of "insights" (in percent) and interest in the task on a scale (1-10), where 1 is "low interest", 10 is "high interest in the task".

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4. Fig. 3. Scheme of the comparisons. Fo/Fz - recording of background activity of the electroencephalogram in the state with open/closed eyes; AUT - alternative uses task, the numbers 1/2 indicate repetitions of protocols with different stimuli; nBFB/SHAM – session of controlling the power of one’s own α-rhythm/session of controlling someone else’s α-rhythm, “dummy” training. Group T/Group Sh – test group/control (SHAM) group. I — testing the hypothesis about the absence of differences between the groups in the states of quiet wakefulness with open/closed eyes before nBFB/SHAM sessions; II — testing the hypothesis about the absence of differences between the groups in performing the cognitive task before nBFB/SHAM sessions; III(a, b) — testing the hypotheses a) about changes in spectral power in the states of quiet wakefulness in the test group after one nBFB session and b) about the absence of such changes in the control group after the SHAM session; IV(a, b) — testing of hypotheses a) about changes in spectral power during cognitive task performance in the test group after nBFB session and b) about absence of such changes in the control group after SHAM session; V — testing of hypothesis about differences between groups in states of quiet wakefulness with open/closed eyes after nBFB/SHAM sessions; VI — testing of hypothesis about differences between groups during cognitive task performance after nBFB/SHAM sessions.

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5. Fig. 4. Intergroup differences in electroencephalogram (EEG) power in β2-range with closed eyes after one nBFB/SHAM session. β2 — EEG frequency range (18–30 Hz). The downward triangle at the corresponding lead indicates a lower power value (according to the post-hoc analysis) in the test group compared to the SHAM group after the nBFB/SHAM session.

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6. Fig. 5. Decrease in the spectral power in the Δ-range of the EEG in the test group after one nBFB session (closed eyes). Δ is the frequency range of the EEG (1.5–4 Hz). The downward triangle at the corresponding lead indicates a lower power value in the AFTER nBFB state compared to the BEFORE state.

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7. Fig. 6. Time-frequency difference maps when comparing the event-related synchronization of electroencephalograms (EEG) of the test (A) and control (B) groups during the AUT task AFTER the nBFB/SHAM session compared to BEFORE. Fp1–O2 — electrode positions (according to the 10/20 system), on each graph, the x-axis shows time (ms): one scale division is 200 ms, vertical lines indicate the beginning and end of stimulus presentation (presentation duration is 400 ms); the y-axis shows frequency (Hz). The tone scale corresponds to the difference in EEG power (conventional units). Black color indicates a decrease in power/desynchronization; white color indicates an increase in power/synchronization. ↓ — the beginning of stimulus presentation is 300 ms after the start of the trial.

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