By Goretti Hurtado Barbeito
Understanding Neurofeedback: A Tool for Enhancing Brain Function in Children
Neurofeedback (NF) is a form of biofeedback that trains individuals to regulate their brain functions by monitoring brain waves and delivering feedback signals, typically in audio or video formats. When the brain exhibits desirable activity, positive feedback is given, while undesirable brain activity results in negative feedback (Marzbani et al., 2016). NF has gained traction as a non-invasive intervention to support children dealing with attention, behavioural, and learning challenges. However, parents may naturally wonder whether NF is appropriate for young children, particularly those under six. How does brain development influence the timing of NF training? What role do EEG patterns play in determining when NF is safe and effective?
This article will explore brain development in young children with a focus on EEG changes and how these changes relate to NF training. We will examine the limitations and developmental concerns associated with NF in very young children and discuss why NF is generally recommended for children aged six and older.
Brain Development in Young Children
Between ages one and six, children experience significant brain development (Thompson & Nelson, 2001). These changes are observable through electroencephalography (EEG), a non-invasive technique that measures electrical activity in the brain. Brainwave patterns in children help us understand cognitive and emotional functions, guiding interventions like NF to optimise outcomes while respecting developmental stages.
• The Theta-Beta Ratio (TBR)
One commonly observed EEG pattern in NF studies is the Theta/Beta ratio. Theta waves (4–7 Hz) are linked with relaxation, daydreaming, and lower levels of attentional control, whereas Beta waves (13–30 Hz) are associated with focus, active thinking, and problem-solving (Abhang et al., 2016). In young children under six, Theta waves are naturally dominant, explaining why they are often imaginative, easily distracted, and emotionally expressive (Cellier et al., 2021). Around age six, a developmental shift occurs, increasing Alpha and Beta wave activity, particularly in the frontal lobes (Rempe et al., 2023). This shift supports enhanced attention and self-control, gradually balancing the Theta-Beta ratio and allowing children to sustain focus for longer periods.
• Sensory-Motor Rhythm (SMR)
The Sensory-Motor Rhythm (SMR), observed at frequencies around 12–15 Hz, is another EEG frequency relevant in NF. SMR is associated with a calm but focused mental state, important for attention and emotional regulation (Mudgal et al., 2020). For children under six, SMR is less developed because the brain's focus remains on basic sensory processing and motor functions. Over time, increased SMR enhances a child's ability to regulate behaviour and impulses, making NF targeting SMR more feasible in older children (Gadea et al., 2020).
• Beta Waves in the Frontal Lobes
Beta waves, critical for cognitive control and attention, gradually increase as children grow. Between ages six and seven, Alpha and Beta activity become more prominent in the brain. By around age 12, Beta activity predominates in the frontal lobes, which are responsible for tasks such as planning, self-control, and decision-making (Karakaş, 2024). Before this developmental milestone, young children lack Beta wave dominance in the frontal areas, which means their ability to focus and regulate behaviour is still maturing.
Image from Mergin Into Onenses, created with EnergeiaX
Why Brain Interventions Are Not Recommended for Children Under Six
Given that the young brain is in a dynamic developmental phase—especially regarding Beta waves and SMR—brain interventions like NF require caution. Several key factors explain why NF and similar brain-based interventions are not generally recommended for children younger than six.
• Brain Plasticity and Sensitivity
Children’s brains exhibit high plasticity, meaning they are exceptionally responsive to environmental influences (Kolb et al., 2017). While plasticity is crucial for learning, it also implies heightened vulnerability to external interventions. Attempts to alter EEG patterns in children under six, such as increasing Beta activity, could interfere with natural developmental processes, potentially causing unintended effects like disruptions in attention or emotional regulation (St. Louis et al., 2016).
• Immature Brainwave Patterns
For children under six, brain activity is naturally dominated by Theta waves, supporting creativity, daydreaming, and free exploration (Oreko et al., 2006). Interventions that alter these natural brainwave patterns could disrupt developmental balance. For instance, prematurely increasing Beta activity might interfere with a young child's imaginative play—a key aspect of early cognitive and social development. Consequently, children benefit from a period in which their Theta-dominant brain can mature without the influence of targeted NF adjustments (St. Louis et al., 2016).
• Cognitive Readiness for Training
NF requires a certain level of cognitive maturity and self-regulation (Weber et al., 2020). For children under six, this cognitive readiness is often lacking (Patel et al., 2017), reducing the likelihood of successful NF engagement. Effective NF training necessitates focus and participation, which young children may find challenging, thereby limiting the intervention’s effectiveness.
Research on Neurofeedback and Age: When Is It Safe to Start?
Many NF studies on children, particularly those with ADHD or attention-related concerns, indicate that NF is most effective when initiated at age seven or older. Research findings provide a clear picture of age thresholds in NF application. Table 1 below shows the average age range of child participants in neurofeedback training studies in the existing literature. All studies focus on children over the age of six, highlighting the significance of brain development in determining suitable ages for neurofeedback interventions. This indicates that older children, especially those around seven and older, are generally more cognitively and physiologically prepared for neurofeedback training.
Table 1. Age Range of Child Participants in Neurofeedback Training Studies.
Key: ADHD = Attention-deficit/hyperactivity disorder; PTSD = post-traumatic stress disorder; ASD = autism spectrum disorder.
Why Neurofeedback Is a Safe Option—At the Right Age
Although NF is not recommended for children under six, it can be a safe and effective option for older children facing attention, behavioral, or emotional challenges. The effectiveness of NF at this age is due to its alignment with natural brain maturation, reinforcing healthy brainwave patterns rather than forcing premature changes (Hill et al., 2022).
Non-invasive and safe: NF is a gentle, non-invasive technique that trains the brain to self-regulate without medication or surgery (Hammond, 2007).
No harmful side effects: Unlike medications, NF does not carry known side effects, making it a safe choice for improving brain function over time (Leem et al., 2021).
Supports focus and attention: NF can enhance attention, reduce impulsivity, and promote cognitive functioning by helping children optimise their Theta-Beta ratio (Nan et al., 2022).
NF Training in Children Under Age Six: Developmental Concerns
• Brain Plasticity and Developmental Vulnerability
As anticipated above, young children’s neuroplasticity means they are particularly receptive to environmental input, including NF interventions. While beneficial for learning, this adaptability also means young children are susceptible to unintended changes in brainwave patterns. Training in young children under six may disrupt their natural EEG patterns, which is why interventions are ideally deferred until developmental patterns stabilise (Strehl et al., 2017). This precaution helps protect their natural growth processes and ensures that any interventions align with their evolving neurological needs.
• Premature Alteration of EEG Patterns
Natural maturation of the Theta-Beta ratio and SMR activity is a hallmark of cognitive and emotional readiness for more complex tasks (Herrera-Morales et al., 2023). Artificially altering these EEG patterns too early could interfere with the developmental shifts crucial to attention regulation and emotional control, possibly resulting in later cognitive issues (Fox & Davidson, 1988). Similarly, EEG patterns related to brain regions such as the anterior cingulate cortex (ACC) and default mode network (DMN) are implicated in various developmental processes. The anterior cingulate cortex is involved in cognitive control, emotional regulation, and error detection (Bush et al., 2000). In early brain development, the activity in the ACC, reflected in EEG patterns such as theta waves or alpha rhythms, has been associated with attention regulation and emotional responses (Luu et al., 2004). The default mode network (DMN) is a network of brain regions active during rest and internal-focused activities, such as self-reflection and mind-wandering (Gusnard & Raichle, 2001). The theta and alpha rhythms often reflect DMN activity, and this network undergoes significant changes during early development (Fan et al., 2021). Therefore, intervening with neurofeedback or other methods during early brain development, particularly before these networks and rhythms stabilise, could influence cognitive, emotional, and social development, making it important to approach these interventions with caution in younger children
• Overtraining Risks
NF generally requires repeated sessions to reinforce brainwave modifications. In young children, excessive NF training could cause an imbalance in EEG activity, with side effects such as irritability, mood swings, or concentration difficulties. Since young children are still developing self-regulation skills, overtraining carries the risk of developmental complications such as emotional dysregulation and impaired attention span, underscoring the importance of age-based NF protocols (Loo & Barkley, 2005).
Other Considerations for NF Training in Young Children
Research consistently supports restricting NF interventions to children older than six, as shown in Table 1 above. Another key reason for this recommendation is the issue of informed consent, which becomes particularly complex with younger children. While children under 18 are not legally required to give full consent, they must provide assent - agreeing to participate in a way that aligns with their developmental understanding. However, children under six may lack the cognitive maturity needed to fully grasp the neurofeedback process. Since cognitive maturity develops throughout adolescence (Johnson et al., 2009), young children may struggle to engage meaningfully or express discomfort during sessions. This lack of an ability to engage means they may not fully benefit from the training, further justifying the recommendation to approach NF with caution for children under six. As established protocols and outcomes are more reliable for this age group, research efforts should focus on children over six.
Future Directions and Recommendations
The potential of NF for enhancing cognitive, emotional, and physiological function in children is clear, yet current best practices suggest restricting NF to children aged six and older. Continued research is essential to understand NF's long-term impacts on neurodevelopment. Some areas of focus may include:
Age-appropriate protocols: NF methods that respect the natural maturation process of the young brain.
Longitudinal studies: To assess NF’s effects on cognition, behavior, and emotions across developmental stages.
Personalised interventions: Tailoring NF to account for developmental stages and individual EEG baselines.
Conclusion: Why Age Matters
Neurofeedback has shown efficacy for a variety of cognitive and emotional conditions, but due to the delicate nature of early brain development, NF in children under six is generally avoided. EEG patterns like the Theta-Beta ratio and SMR activity are still evolving in this age group, and premature intervention risks developmental disruption. Ethical considerations and the potential for unintended side effects support the common age restriction for NF use in children. As NF technology advances, future research may clarify safe and effective applications for younger populations. Until then, NF remains most appropriate for children aged six and older, aligning with.
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Reference
Abhang, P. A., Gawali, B. W., & Mehrotra, S. C. (2016). Technological Basics of EEG Recording and Operation of Apparatus. In Introduction to EEG- and Speech-Based Emotion Recognition (pp. 19–50). Elsevier. https://doi.org/10.1016/B978-0-12-804490-2.00002-6
Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences, 4(6), 215–222. https://doi.org/10.1016/S1364-6613(00)01483-2
Cellier, D., Riddle, J., Petersen, I., & Hwang, K. (2021). The development of theta and alpha neural oscillations from ages 3 to 24 years. Developmental Cognitive Neuroscience, 50, 100969. https://doi.org/10.1016/j.dcn.2021.100969
Drysdale, M. T. B., Martinez, Y. J., & Thompson, L. (2012). The Effects of Humorous Literature on Emotion: A Pilot Project Comparing Children with Asperger’s Syndrome Before and After Neurofeedback Training and Controls. Journal of Neurotherapy, 16(3), 196–209. https://doi.org/10.1080/10874208.2012.705758
Duric, N. S., Assmus, J., Gundersen, D., Duric Golos, A., & Elgen, I. B. (2017). Multimodal treatment in children and adolescents with attention-deficit/hyperactivity disorder: a 6-month follow-up. Nordic Journal of Psychiatry, 71(5), 386–394. https://doi.org/10.1080/08039488.2017.1305446
Fan, F., Liao, X., Lei, T., Zhao, T., Xia, M., Men, W., Wang, Y., Hu, M., Liu, J., Qin, S., Tan, S., Gao, J.-H., Dong, Q., Tao, S., & He, Y. (2021). Development of the default-mode network during childhood and adolescence: A longitudinal resting-state fMRI study. NeuroImage, 226, 117581. https://doi.org/10.1016/j.neuroimage.2020.117581
Gadea, M., Aliño, M., Hidalgo, V., Espert, R., & Salvador, A. (2020). Effects of a single session of SMR neurofeedback training on anxiety and cortisol levels. Neurophysiologie Clinique, 50(3), 167–173. https://doi.org/10.1016/j.neucli.2020.03.001
Geladé, K., Bink, M., Janssen, T. W. P., van Mourik, R., Maras, A., & Oosterlaan, J. (2017). An RCT into the effects of neurofeedback on neurocognitive functioning compared to stimulant medication and physical activity in children with ADHD. European Child & Adolescent Psychiatry, 26(4), 457–468. https://doi.org/10.1007/s00787-016-0902-x
Gusnard, D. A., & Raichle, M. E. (2001). Searching for a baseline: Functional imaging and the resting human brain. Nature Reviews Neuroscience, 2(10), 685–694. https://doi.org/10.1038/35094500
Hammond, D. C. (2007). What Is Neurofeedback? Journal of Neurotherapy, 10(4), 25–36. https://doi.org/10.1300/J184v10n04_04
Herrera-Morales, W. V., Reyes-López, J. V., Tuz-Castellanos, K. N.-H., Ortegón-Abud, D., Ramírez-
Lugo, L., Santiago-Rodríguez, E., & Núñez-Jaramillo, L. (2023). Variations in Theta/Beta Ratio and Cognitive Performance in Subpopulations of Subjects with ADHD Symptoms: Towards Neuropsychological Profiling for Patient Subgrouping. Journal of Personalized Medicine, 13(9). https://doi.org/10.3390/jpm13091361
Hill, A. T., Clark, G. M., Bigelow, F. J., Lum, J. A. G., & Enticott, P. G. (2022). Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood. Developmental Cognitive Neuroscience, 54, 101076. https://doi.org/10.1016/j.dcn.2022.101076
Johnson, S. B., Blum, R. W., & Giedd, J. N. (2009). Adolescent Maturity and the Brain: The Promise and Pitfalls of Neuroscience Research in Adolescent Health Policy. Journal of Adolescent Health, 45(3), 216–221. https://doi.org/10.1016/j.jadohealth.2009.05.016
Karakaş, S. (2024). A Review of Childhood Developmental Changes in Attention as Indexed in the Electrical Activity of the Brain. Brain Sciences, 14(5). https://doi.org/10.3390/brainsci14050458
Kolb, B., Harker, A., & Gibb, R. (2017). Principles of plasticity in the developing brain. Developmental Medicine & Child Neurology, 59(12), 1218–1223. https://doi.org/10.1111/dmcn.13546
Kouijzer, M. E. J., van Schie, H. T., de Moor, J. M. H., Gerrits, B. J. L., & Buitelaar, J. K. (2010). Neurofeedback treatment in autism. Preliminary findings in behavioral, cognitive, and neurophysiological functioning. Research in Autism Spectrum Disorders, 4(3), 386–399. https://doi.org/10.1016/j.rasd.2009.10.007
Leem, J., Cheong, M. J., Lee, H., Cho, E., Lee, S. Y., Kim, G.-W., & Kang, H. W. (2021). Effectiveness, Cost-Utility, and Safety of Neurofeedback Self-Regulating Training in Patients with Post-Traumatic Stress Disorder: A Randomized Controlled Trial. Healthcare, 9(10), 1351. https://doi.org/10.3390/healthcare9101351
Luu, P., Tucker, D. M., & Makeig, S. (2004). Frontal midline theta and the error-related negativity: neurophysiological mechanisms of action regulation. Clinical Neurophysiology, 115(8), 1821–1835. https://doi.org/10.1016/j.clinph.2004.03.031
Marzbani, H., Marateb, H. R., & Mansourian, M. (2016). Neurofeedback: A Comprehensive Review on System Design, Methodology and Clinical Applications. Basic and Clinical Neuroscience, 7(2), 143–158. https://doi.org/10.15412/J.BCN.03070208
Moreno-García, I., Meneres-Sancho, S., Camacho-Vara de Rey, C., & Servera, M. (2019). A Randomized Controlled Trial to Examine the Posttreatment Efficacy of Neurofeedback, Behavior Therapy, and Pharmacology on ADHD Measures. Journal of Attention Disorders, 23(4), 374–383. https://doi.org/10.1177/1087054717693371
Mudgal, S. K., Sharma, S. K., Chaturvedi, J., & Sharma, A. (2020). Brain computer interface advancement in neurosciences: Applications and issues. Interdisciplinary Neurosurgery, 20, 100694. https://doi.org/10.1016/j.inat.2020.100694
Nan, W., Wan, M., Jiang, Y., Shi, X., Wan, F., & Cai, D. (2022). Alpha/Theta Ratio Neurofeedback Training for Attention Enhancement in Normal Developing Children: A Brief Report. Applied Psychophysiology and Biofeedback, 47(3), 223–229. https://doi.org/10.1007/s10484-022-09550-6
OREKHOVA, E., STROGANOVA, T., POSIKERA, I., & ELAM, M. (2006). EEG theta rhythm in infants and preschool children. Clinical Neurophysiology, 117(5), 1047–1062. https://doi.org/10.1016/j.clinph.2005.12.027
Patel, D. R., Soares, N., & Wells, K. (2017). Neurodevelopmental readiness of children for participation in sports. Translational Pediatrics, 6(3), 167–173. https://doi.org/10.21037/tp.2017.05.03
Rempe, M. P., Ott, L. R., Picci, G., Penhale, S. H., Christopher-Hayes, N. J., Lew, B. J., Petro, N. M., Embury, C. M., Schantell, M., Johnson, H. J., Okelberry, H. J., Losh, K. L., Willett, M. P., Losh, R. A., Wang, Y.-P., Calhoun, V. D., Stephen, J. M., Heinrichs-Graham, E., Kurz, M. J., & Wilson, T. W. (2023). Spontaneous cortical dynamics from the first years to the golden years. Proceedings of the National Academy of Sciences of the United States of America, 120(4), e2212776120. https://doi.org/10.1073/pnas.2212776120
Rogel, A., Loomis, A. M., Hamlin, E., Hodgdon, H., Spinazzola, J., & van der Kolk, B. (2020). The impact of neurofeedback training on children with developmental trauma: A randomized controlled study. Psychological Trauma: Theory, Research, Practice, and Policy, 12(8), 918–929. https://doi.org/10.1037/tra0000648
Schuurmans, A. A. T., Nijhof, K. S., Scholte, R., Popma, A., & Otten, R. (2020). A novel approach to improve stress regulation among traumatized youth in residential care: Feasibility study testing three game‐based meditation interventions. Early Intervention in Psychiatry, 14(4), 476–485. https://doi.org/10.1111/eip.12874
St. Louis, E., Frey, L., Britton, J., Hopp, J., Korb, P., Koubeissi, M., Lievens, W., & Pestana-Knight, E. (2016). Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants. American Epilepsy Society. https://doi.org/10.5698/978-0-9979756-0-4
Steiner, N. J., Frenette, E., Hynes, C., Pisarik, E., Tomasetti, K., Perrin, E. C., & Rene, K. (2014). A Pilot Feasibility Study of Neurofeedback for Children with Autism. Applied Psychophysiology and Biofeedback, 39(2), 99–107. https://doi.org/10.1007/s10484-014-9241-1
Strehl, U., Aggensteiner, P., Wachtlin, D., Brandeis, D., Albrecht, B., Arana, M., Bach, C., Banaschewski, T., Bogen, T., Flaig-Röhr, A., Freitag, C. M., Fuchsenberger, Y., Gest, S., Gevensleben, H., Herde, L., Hohmann, S., Legenbauer, T., Marx, A.-M., Millenet, S., … Holtmann, M. (2017). Neurofeedback of Slow Cortical Potentials in Children with Attention-Deficit/Hyperactivity Disorder: A Multicenter Randomized Trial Controlling for Unspecific Effects. Frontiers in Human Neuroscience, 11. https://doi.org/10.3389/fnhum.2017.00135
Thompson, R. A., & Nelson, C. A. (2001). Developmental science and the media: Early brain development. American Psychologist, 56(1), 5–15. https://doi.org/10.1037/0003-066X.56.1.5
Weber, L. A., Ethofer, T., & Ehlis, A.-C. (2020). Predictors of neurofeedback training outcome: A systematic review. NeuroImage. Clinical, 27, 102301. https://doi.org/10.1016/j.nicl.2020.102301
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