Neurofeedback has been employed in recent years as a cognitive learning approach to enhance brain processes for therapeutic or recreational reasons. It involves teaching people to monitor their own brain activity and adjust it in the ways they see fit. The central idea is that by exerting this kind of command over a particular form of brain activity, one can improve the cognitive abilities that are normally associated with it, and one can also cause certain functional and structural transformations in the brain system, assisted by the neuronal plasticity and learning effects. Herein, we discuss the theoretical underpinnings of neurofeedback and outline the practical applications of this technique in clinical and experimental settings. Here, we take a look at the alterations in reinforcement learning cortical networks that have occurred as a result of neurofeedback training, as well as the more general impacts of neurofeedback on certain regions of the brain. Finally, we discuss the current obstacles that neurofeedback research must overcome, such as the need to quantify the temporal neorofeedback dynamics and effects, relate its behavioral patterns to daily life routines, formulate effective controls to differential placebo from actual neurofeedbackimapcts, and enhance the processing of cortical signal to attain fine-grained real-time modeling of cognitive functionalities.
Keywords
Neurofeedback, Attention Deficit Hyperactivity Disorder, Transcranial Magnetic Stimulation, Deep Brain Stimulation.
Neurofeedback Collaborative Group, “Neurofeedback for attention-deficit/hyperactivity disorder: 25-month follow-up of double-blind randomized controlled trial,” J. Am. Acad. Child Adolesc. Psychiatry, 2022.
S. Yu and J. Liu, “Ensemble calibration model of near-infrared spectroscopy based on functional data analysis,” Spectrochim. Acta A Mol. Biomol. Spectrosc., vol. 280, no. 121569, p. 121569, 2022.
J. S. Gomes et al., “Hemoencephalography self-regulation training and its impact on cognition: A study with schizophrenia and healthy participants,” Schizophr. Res., vol. 195, pp. 591–593, 2018.
C. Metz et al., “Reproducibility of non-contrast enhanced multi breath-hold ultrashort echo time functional lung MRI,” Magn. Reson. Imaging, vol. 98, pp. 149–154, 2023.
Y. Matsuda et al., “Repetitive transcranial magnetic stimulation for preventing relapse in antidepressant treatment-resistant depression: A systematic review and meta-analysis of randomized controlled trials,” Brain Stimul., vol. 16, no. 2, pp. 458–461, 2023.
M. B. Ramos, J. P. E. Britz, M. Mattana, P. H. Pires de Aguiar, and P. R. Franceschini, “An unusual early and persistent symptomatic presentation of Peri-lead edema following deep brain stimulation: Case report and literature review,” Deep Brain Stimulation, 2022.
P. S. Foster and V. Drago, “Handbook of neurofeedback: Dynamics and clinical applications,” Arch. Clin. Neuropsychol., vol. 24, no. 2, pp. 194–195, 2009.
M. Garcia Pimenta, T. Brown, M. Arns, and S. Enriquez-Geppert, “Treatment efficacy and clinical effectiveness of EEG neurofeedback as a personalized and multimodal treatment in ADHD: A critical review,” Neuropsychiatr. Dis. Treat., vol. 17, pp. 637–648, 2021.
Y. Kolken, P. Bouny, and M. Arns, “Effects of SMR neurofeedback on cognitive functions in an adult population with sleep problems: A Tele-neurofeedback study,” Appl. Psychophysiol. Biofeedback, vol. 48, no. 1, pp. 27–33, 2023.
K. Schmidt et al., “Cancer patients’ age-related benefits from mobile neurofeedback-therapy in quality of life and self-efficacy: A clinical waitlist control study,” Appl. Psychophysiol. Biofeedback, 2022.
“Formal vs. Processing approaches to syntactic phenomena: A special issue of the journalLanguage and cognitive processes,” Lang. Cogn. Process., vol. 28, no. 3, pp. 221–221, 2013.
M. I. Posner, B. E. Sheese, Y. Odludaş, and Y. Tang, “Analyzing and shaping human attentional networks,” Neural Netw., vol. 19, no. 9, pp. 1422–1429, 2006.
W. Min, Y. Maoquan, W. Xiaojun, G. Kui, and Z. Xiao, “A radio communication system for neuronal signals,” China Popul. Resour. Environ., vol. 10, no. 3, pp. 125–128, 2012.
S. M. Snyder, H. Quintana, S. B. Sexson, P. Knott, A. F. M. Haque, and D. A. Reynolds, “Blinded, multi-center validation of EEG and rating scales in identifying ADHD within a clinical sample,” Psychiatry Res., vol. 159, no. 3, pp. 346–358, 2008.
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Douglas Klutz, “Neurofeedback for Cognitive Enhancement, Intervention and Brain Plasticity”, Journal of Biomedical and Sustainable Healthcare Applications, vol.3, no.1, pp. 045-055, January 2023. doi: 10.53759/0088/JBSHA202303005.