Brainwaves Activities during Resting State: A Neurofeedback Case Study

Authors

  • Nur Afiqah Mohd Radi University Malaysia Sarawak
  • Nurul Hanim Nasaruddin Universiti Malaysia Sarawak

DOI:

https://doi.org/10.33736/jcshd.4004.2022

Keywords:

resting state, neurofeedback, electroencephalogram

Abstract

Neurofeedback training (NFT) on a healthy participant was used to analyse brainwave activity during resting state. Electrodes were placed on the prefrontal cortex and at reference sites to conduct the experiment. The patient was asked to remain still and rest by sitting in a chair. The technology was then used to record the electrical waves emitted by brain activities. After ten cycles with a break interval, theta, alpha, and high beta brainwaves were discovered. There were statistically significant brainwaves in the prefrontal cortex. Remarkably, the ascending high beta oscillation range was the highest, while the alpha wave was the lowest, contradicting earlier results. Anxiety, excitement, and focus were thought to be associated with the highest range of high beta waves.

References

Aamidfar, M., Azimi, L., Azizi, M. R., & Heysieattalab, S. (2017). Changes in the brain's bioelectrical activity in cognition, consciousness, and mental disorders. Medical Journal of The Islamic Republic of Iran, 31(53), 307-312. https://doi.org/10.14196/mjiri.31.53

https://doi.org/10.14196/mjiri.31.53

Angelidis, A., Barry, R. J., Blasio, F. M. D., Fogarty, J. S., Putman, P., & Son, D. V. (2019). Frontal EEG theta/beta ratio during mind-wandering episodes. Biological Psychology, 140, 19-27. https://doi.org/10.1016/j.biopsycho.2018.11.003

https://doi.org/10.1016/j.biopsycho.2018.11.003

Arman, F., Balcisoy, S., Cetin, M., Ekici, B., Eroglu, G., & Gurkan, M. (2018). Can we predict who will respond more to neurofeedback with resting state EEG?. 2018 Medical Technologies National Congress (TIPTEKNO). http://dx.doi.org/10.1109/TIPTEKNO.2018.8596857

https://doi.org/10.1109/TIPTEKNO.2018.8596857

Barry, R. J., Clarke, A. R., Johnstone, S. J., Magee, C. A., & Rushby, J. A. (2007). EEG differences between eyes-closed and eyes-open resting conditions. Clinical Neurophysiology, 118(12), 2765-2773. https://doi.org/10.1016/j.biopsycho.2017.09.010

https://doi.org/10.1016/j.biopsycho.2017.09.010

Barry, R. J., Karamacoska, D., & Steiner, G. Z. (2017). Resting state intrinsic EEG impacts ongoing stimulus-response processes. Psychophysiology, 54(6), 894-903. https://doi.org/10.1111/psyp.12851

https://doi.org/10.1111/psyp.12851

Barry, R. J., Blasio, F. M. D., Does, W. V. D., Putman, P., & Son, D. V. (2019). Electroencephalography theta/beta ratio covaries with mind wandering and functional connectivity in the executive control network. Annals of the New York Academy of Sciences, 1452(1), 52-64. https://doi.org/10.1111/nyas.14180

https://doi.org/10.1111/nyas.14180

Bastiaansen, M. C. M., Hagoort, P., Norris, D. G., Oostenveld, R., Petersson, K. M., & Scheeringa, R. (2008). Frontal theta EEG activity correlated negatively with the default mode network in the resting state. International Journal of Psychophysiology, 67(3), 242-251. https://doi.org/10.1016/j.ijpsycho.2007.05.017

https://doi.org/10.1016/j.ijpsycho.2007.05.017

Becker, B., Goebel, R., Kendrick, K., Li, J., Li, K., Luhrs, M., Sindermann, C., Yao, S., Zhao, F., Zhao, W., & Zhao, Z. (2019). Real-Time Functional Connectivity-Informed Neurofeedback of Amygdala-Frontal Pathways Reduces Anxiety. Psychotherapy and Psychosomatics, 88(1), 5-15. https://doi.org/10.1159/000496057

https://doi.org/10.1159/000496057

Birn, R. M., Kirk, G. R., Meier, T. B., Meyerand, M. E., Molloy, E. K., Nair, V. A., Patriat, R., & Prabhakaran, V. (2014). The effect of resting condition on resting-state fMRI reliability and consistency: A comparison between resting with eyes open, closed, and fixated. Neuroimage, 78, 463-473. https://doi.org/10.1016/j.neuroimage.2013.04.013

https://doi.org/10.1016/j.neuroimage.2013.04.013

Bischoff, M., Blecker, C., Gebhardt, H., Morgen, K., Sammer, G., Stark, R., & Vaitl, D. (2007). Relationship between Regional Hemodynamic Activity and Simultaneously Recorded EEG-Theta Associated with Mental Arithmetic-Induced Workload. Human Brain Mapping, 28, 793-803. https://doi.org/10.1002/hbm.20309

https://doi.org/10.1002/hbm.20309

Bohnen, J. L., & Sunder, K. R. (2017). The progression of neurofeedback: an evolving paradigm in addiction treatment and relapse prevention. MOJ Addiction Medicine & Therapy, 3(3), 75-78. https://doi.org/10.15406/mojamt.2017.03.00037

https://doi.org/10.15406/mojamt.2017.03.00037

Burke, M. R., Bunce, D., Delvenne, J. F., & Scally, B. (2018). Resting-state EEG power and connectivity are associated with alpha peak frequency slowing in healthy ageing. Neurobiology of Aging, 71, 149-155. https://doi.org/10.1016/j.neurobiolaging.2018.07.004

https://doi.org/10.1016/j.neurobiolaging.2018.07.004

Castanon, A. N., Chai, X .J., Cohen, B. M., Gabrieli, J. D. E., Gabrieli, S. W., McCarthy, J. M., Ongur, D., & Shinn, A. K. (2011). Abnormal Medial prefrontal Cortex Resting-State Connectivity in Bipolar Disorder and Schizophrenia. Neuropsychopharmacology, 36(10), 2009-2017. https://doi.org/10.1038/npp.2011.88

https://doi.org/10.1038/npp.2011.88

Chen, Y., Li, J., Shao, S., Xiao, Y., Wu, J., & Zhou, Q. (2021). Decreased resting-state alpha band activation and functional connectivity after sleep deprivation. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-020-79816-8

https://doi.org/10.1038/s41598-020-79816-8

Cheon, E., Choi, J. H., & Koo, B. H. (2016). The Efficacy of Neurofeedback in Patients with Major Depressive Disorder: An Open Labelled Prospective Study. Applied Psychophysiology and Biofeedback, 41(1), 107-110. https://doi.org/10.1007/s10484-015-9315-8

https://doi.org/10.1007/s10484-015-9315-8

Cherry, K. (2020, June 3). What Is Operant Conditioning and How Does It Work?. Retrieved from https://www.verywellmind.com/operant-conditioning-a2-2794863#:~:text=Operant%20conditioning%20relies%20on%20a,story%20again%20in%20the%20future.

Choi, G. Y., Choi, S. I., & Hwang, H. J. (2018). Individual identification based on resting-state EEG. 2018 6th International Conference on Brain-Computer Interface (BCI). https://doi.org/10.1109/IWW-BCI.2018.8311515

https://doi.org/10.1109/IWW-BCI.2018.8311515

Deijin, J. B., Eichhorn, D., Ejik, L. V., Engelbregt, H. J., Karch, S., Keeser, D., Pogarell, O., & Suiker, E. M. (2016). Short and long-term effects of sham-controlled prefrontal EEG-neurofeedback training in healthy subjects. Clinical Neurophysiology, 124(4), 1931-1937. https://doi.org/10.1016/j.clinph.2016.01.004

https://doi.org/10.1016/j.clinph.2016.01.004

Eades, L. H., Helvig, A., & Wade, S. (2016). Rest and the associated benefits in restorative sleep: a concept analysis. Journal of Advanced Nursing, 72(1), 62-72. https://doi.org/10.1111/jan.12807

https://doi.org/10.1111/jan.12807

Galili, T. (2010, February 22). Post hoc analysis for Friedman's Test (R code). R-statistics blog. Retrieved from https://libanswers.snhu.edu/faq/190823

Ghasemi, A., & Zahediasl, S. (2012). Normality Tests for Statistical Analysis: A Guide for Non-Statisticians. International Journal of Endocrinology Metabolism, 10(2), 486-489. https://dx.doi.org/10.5812%2Fijem.3505

https://doi.org/10.5812/ijem.3505

Greer, J. M. H., Hamilton, C., McMullon, M. E. G., Riby D. M., & Riby, L. M. (2021). An EEG investigation of alpha and beta activity during resting states in adults with Williams syndrome. BMC Psychology, 9, 72. https://doi.org/10.1186/s40359-021-00575-w

https://doi.org/10.1186/s40359-021-00575-w

Gutmann, B., Hildebrand, C., Hollmann, W., Huldunker, T., Mierau, A., & Struder, H. K. (2015). Effects of Physical Exercise on Individual Resting State EEG Alpha Peak Frequency. Neural Plasticity, 2015, 717312. https://doi.org/10.1155/2015/717312

https://doi.org/10.1155/2015/717312

Hampson, M., Gruner, P., Pittenger, C., Saksa, J., Scheinost, D., Stoica, T., & Wasylink, S. (2014). Resting state functional connectivity predicts neurofeedback response. Frontiers in Behavioural Neuroscience, 8, 338. https://dx.doi.org/10.3389%2Ffnbeh.2014.00338

https://doi.org/10.3389/fnbeh.2014.00338

Hara, M., Ikeda, T., Kawakami, K., Kawakami, K., Matsushita, K., Nagase, Y., Nojima, I., Sugata, H., Tsuruta, K., Yagawa, S., & Yagi, K. (2020). Role of beta-band resting-state functional connectivity as a predictor of motor learning ability. NeuroImage, 210, 116562. https://doi.org/10.1016/j.neuroimage.2020.116562

https://doi.org/10.1016/j.neuroimage.2020.116562

He, H., Qiu, S., Wang, S., Yi, W., & Zhang, C. (2020). The Lasting Effects of Low-Frequency Repetitive Transcranial Magnetic Stimulation on Resting State EEG in Healthy Subjects. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(4). 832-841. https://doi.org/10.1109/TNSRE.2020.2977883

https://doi.org/10.1109/TNSRE.2020.2977883

Hsiao, J. H., Lau, E. Y. Y., & Zhang, J. (2018). Sleep deprivation compromises resting-state emotional regulatory processes: An EEG study. Journal of Sleep Research, 28(3). https://doi.org/10.1111/jsr.12671

https://doi.org/10.1111/jsr.12671

Hsieh, S., & Wang, J.R. (2013). Neurofeedback training improves attention and working memory performance. Clinical Neurophysiology, 124(12), 2406-2420. https://doi.org/10.1016/j.clinph.2013.05.020

https://doi.org/10.1016/j.clinph.2013.05.020

Huang, J. (2019). Greater brain activity during the resting state and the control of activation during the performance of tasks. Scientific Reports, 9, 5027. https://doi.org/10.1038/s41598-019-41606-2

https://doi.org/10.1038/s41598-019-41606-2

Jin, Z., Kong, X., Ling, L., Tan, B., & Ping, Y. (2013). The Difference of Brain Functional Connectivity between Eyes-Closed and Eyes-Open Using Graph Theoretical Analysis. Computational and Mathematical Methods in Medicine, 2013, 976365. https://doi.org/10.1155/2013/976365

https://doi.org/10.1155/2013/976365

Jones, D. (2016, July 29). Biofeedback's early history. Retrieved from https://www.biofeedback-tech.com/articles/2016/7/29/biofeedbacks-early-history

Krauz, R., Kublik, E., Rogala, J., & Wrobel, A. (2020). Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance. Scientific Reports, 10,1-15. https://doi.org/10.1038/s41598-020-61866-7

https://doi.org/10.1038/s41598-020-61866-7

Laerd Statistics. (n.d). Friedman Test in SPSS Statistics. Retrieved from https://statistics.laerd.com/spss-tutorials/friedman-test-using-spss-statistics.php

Laerd Statistics. (n.d). Testing for Normality using SPSS Statistics. Retrieved from https://statistics.laerd.com/spss-tutorials/testing-for-normality-using-spss-statistics.php

Limbrick, D. D., Raichle, M. E., Schlaggar, B. L., Shah, M. N., Shimony, J. S., Smyth, M. D., Snyder, A. Z., & Pizoli, C. E. (2011). Resting-state activity in development and maintenance of normal brain function. Proceedings of the National Academy of Sciences, 108(28), 11638-11643. https://doi.org/10.1073/pnas.1109144108

https://doi.org/10.1073/pnas.1109144108

Mansourian, M., Marateb, H.R., & Marzbani, H. (2016). Neurofeedback: A Comprehensive Review on System Design, Methodology and Clinical Applications. Basic and Clinical Neuroscience Journal, 7(2). 143-158. https://doi.org/10.15412/J.BCN.03070208

https://doi.org/10.15412/J.BCN.03070208

Piantoni, G., Romenjin, N., Smit, D. J., Someren, E. J. V., Werf, Y. D. V. D., & Verweij, I. M. (2014). Sleep deprivation leads to a loss of functional connectivity in frontal brain regions. BMC Neuroscience, 15, 88. https://doi.org/10.1186/1471-2202-15-88

https://doi.org/10.1186/1471-2202-15-88

Sattar, FA., & Valdiya, PS. (2017). Biofeedback In Medical Practice. Medical Journal Armed Forces India, 55(1), 51-54. https://dx.doi.org/10.1016%2FS0377-1237(17)30315-5

https://doi.org/10.1016/S0377-1237(17)30315-5

Sleep.org. (2021, March 16). Resting vs Sleeping. Retrieved from https://www.sleep.org/resting-vssleeping/#:~:text=Rest%20has%20a%20broader%20definition,level%20of%20disengagement %20as%20sleep.

Strehl, U. (2014). What learning theories can teach us in designing neurofeedback treatments. Frontiers in Human Neuroscience, 8. 894. https://doi.org/10.3389/fnhum.2014.00894

https://doi.org/10.3389/fnhum.2014.00894

Downloads

Published

2022-03-31

How to Cite

Mohd Radi, N. A., & Nasaruddin, N. H. (2022). Brainwaves Activities during Resting State: A Neurofeedback Case Study. Journal of Cognitive Sciences and Human Development, 8(1), 100–111. https://doi.org/10.33736/jcshd.4004.2022