Skip to main content
Download PDF
Download ePub

Surface electroencephalographic neurofeedback improves sustained attention in ADHD: a meta-analysis of randomized controlled trials

Child and Adolescent Psychiatry and Mental Health volume 16, Article number: 104 (2022) Cite this article

Abstract

Background

The efficacy of surface electroencephalographic neurofeedback (EEG-NF) for improving attentional performance assessed by laboratory measures in patients with attention-deficit/hyperactivity disorder (ADHD) remains unclear.

Methods

Following the PRISMA guidelines, the PubMed, Embase, ClinicalKey, Cochrane CENTRAL, ScienceDirect, Web of Science, and ClinicalTrials.gov databases were systematically searched for randomized controlled trials on the efficacy of surface EEG-NF against ADHD focusing on attentional performance evaluated by laboratory measures from inception to January 2022.

Results

Fourteen eligible studies were analyzed. Of the 718 participants involved, 429 diagnosed with ADHD received EEG-NF treatment. Significant improvement in attentional performance in ADHD subjects receiving EEG-NF was noted compared to their comparators (p < 0.01). Besides, there was a significant EEG-NF-associated beneficial effect on sustained attention (Hedges’ g = 0.32, p < 0.01), whereas the impact on selective attention (p = 0.57) and working memory (p = 0.59) was limited. Moreover, protocol including beta wave enhancement was superior to that only focusing on reducing theta/beta ratio or modulation of slow cortical potential. Subgroup analyses showed that three sessions per week of EEG-NF produced the best effect, while the efficacy of surface EEG-NF was much poorer (Hedges’ g = 0.05) when only studies that blinded their participants from knowledge of treatment allocation were included. No significant difference was noted in the improvement of attentional performance 6–12 months after EEG-NF intervention (n = 3, p = 0.42).

Conclusions

Our results demonstrated the satisfactory effectiveness of surface EEG-NF for improving sustained attention, especially when beta wave enhancement was included, despite its failure to sustain a long-term effect. Further large-scale trials are warranted to support our findings.

Background

Attention-deficit/hyperactivity disorder (ADHD), which is characterized by symptoms of inattention and/or hyperactivity/impulsivity, is one of the most common neurodevelopmental disorders in children and adolescents [1]. Despite the reported efficacy of pharmacological interventions in a number of meta-analyses and reviews [2], parents of children with ADHD still frequently seek alternative treatments due to concerns about medication-related side effects [3]. Electroencephalographic neurofeedback (EEG-NF), which has been shown to be a promising alternative therapeutic option for symptoms of ADHD in previous randomized controlled trials (RCTs) and meta-analyses [4, 5], is a kind of biofeedback involving self-regulation of brain activity by providing an audio/visual feedback signal in response to the measured brain waves [6].

However, although previous evidence seemed to support the therapeutic effectiveness of EEG-NF for the symptoms of ADHD [6, 7], the treatment effect of EEG-NF was mostly evaluated with behavioral rating scales and only a few RCTs provided laboratory measures of attentional performance [6, 7]. While the former is rated by most proximal (i.e., parents) and/or possibly blind (e.g., teachers) evaluators, the latter involves computerized or paper neurocognitive tests as an objective assessment of the patient’s clinical condition [8]. Based on behavioral rating scales, the majority of previous meta-analytical studies [5, 7, 9,10,11,12,13] demonstrated positive treatment effects from the most-proximal evaluators, while the results from possibly blinded evaluators were inconsistent [5, 7, 9,10,11,12,13]. In contrast, an objective or computerized performance test may be less susceptible to informant bias [14] despite the lack of tangible evidence to support this proposal. In addition, instead of being a simple definition, the concept of attention is too complex [15] to be thoroughly evaluated with behavioral rating scales that could not provide specific information about the different aspects of attentional functions.

With regard to the treatment protocols of EEG-NF for ADHD, there are several strategies that target different patterns of brain waves or their combinations [e.g., theta/beta (TB)] [6, 16]. The TB protocol aiming at controlling hyperactivity and enhancing concentration [17] is one of the most popular options for patients with ADHD [6]. The slow cortical potential (SCP) protocol, which involves SCP modulation, has also been shown to improve the clinical symptoms of ADHD [18]. On the other hand, a recent study reported that frontal beta activity may be a better training target of EEG-NF compared to the TB protocol in ADHD patients with a longer reaction time [19]. Nevertheless, the treatment efficacy of the TB protocol in comparison with that of other therapeutic strategies remains unclear.

Notwithstanding the non-invasiveness of EEG-NF, its efficacy against the symptoms of ADHD has been controversial because of inconclusive evidence attributable to possible evaluator bias [20]. Some studies using a double-blind design failed to show superior effectiveness of EEG-NF compared to that of sham controls [21, 22]. There are also criticisms against its cost-effectiveness, time-consuming nature, and lack of long-lasting benefits [6]. Therefore, focusing on studies that used laboratory measures for theoretically more objective assessment of improvements in different components of attentional performance from surface EEG-NF, the present meta-analysis aimed at elucidating its therapeutic benefits in patients with ADHD. Furthermore, we also investigated the impacts of the intensity of treatment (i.e., number of sessions per week), quality of blinding, and different treatment protocols (i.e., beta vs. TB) on the therapeutic outcome in an attempt to provide the latest evidence for guiding clinical practice.

Methods

Study eligibility and definitions

For our literature search, we used different keywords, namely “neurofeedback” AND “attention or attention-deficit/hyperactivity disorder or ADHD” for identifying clinical trials (Additional file 1: Table S1). The criteria for study eligibility were: (1) random assignment of participants diagnosed as having ADHD to different treatment groups, (2) a comparison between subjects receiving EEG-NF and their comparators who were subjected to active or inactive treatments [e.g., waitlist or treatment as usual (TAU)])] other than pharmacological interventions, and (3) inclusion of attentional performance as an outcome measure of attention tests or cognitive tasks.

Electronic searches

In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [23, 24], we systematically searched the PubMed, Embase, ClinicalKey, Cochrane (CENTRAL), ScienceDirect, Web of Science, and ClinicalTrials.gov databases for English articles from inception to January 2022. The current study was registered with the international prospective register of meta-analysis (PROSPERO CRD 42021247674). The PRISMA checklist of the current meta-analysis is shown in Additional file 2: Table S2.

Data extraction and management

Two authors (Cheng YS and Yeh PY) completed the title and abstract screening. Besides, full-text screening was independently conducted by Cheng YS and Yeh PY. Kappa statistics was calculated to assess inter-rater reliability [25]. Any disagreements were resolved through discussion between the two authors until consensus was reached. In case of missing information, we tried to electronically contact the corresponding authors for the original data. On encountering duplicated data, the article with the largest sample size or the latest information was chosen for analysis.

The validity of the eligible studies was assessed with the six criteria of the risk of bias assessment tool developed by the Cochrane Collaboration to target possible sources of bias [26], including random sequence generation, concealment of allocation to conditions, performance bias, detection bias, attrition bias, and reporting bias.

Data synthesis and sensitivity analysis

Improvement in attentional performance was quantitatively expressed as effect size (ES) based on Hedges’ g. We used the computer program “Comprehensive Meta-Analysis version for Windows (CMA, version 3.3.070)” to calculate ESs, which were assigned a positive sign to indicate an improvement in attentional performance in subjects receiving EEG-NF. The outcomes of different assessment tools for testing attentional performance were categorized into three domains: (1) sustained attention, (2) selective attention, and (3) working memory. If a study provided data on only one domain, the data (merged when more than one set of data from different assessment tools) were used for the analysis of overall attentional performance. On the other hand, if a study offered results of more than one domain (e.g., integrated visual auditory continuous performance test for sustained attention and Stroop word and color test for selective attention), the results were standardized and averaged to produce a single ES. Regarding the evaluation of differences in magnitude of attention improvement, ESs of 0.8, 0.5, and 0.2 were interpreted as large, moderate, and small, respectively [27]. Besides, to tackle the problem of a diminished statistical power due to a small sample size commonly observed in studies involving psychological treatments, we used a random-effects model to estimate the ESs in this meta-analysis by assuming identical true ESs in all studies [28, 29]. This model allows the adjustment of sample size bias by offering an average distribution of effects across the included studies [29] so that the weights given to the studies may be more similar to each other [28].

In the current meta-analysis, we also performed subgroup analyses based on a random-effects model [28] to identify potential factors that may influence the observed therapeutic effectiveness of EEG-NF by categorizing the included studies according to: (1) Different treatment intensities defined as the number of NF sessions per week, (2) With or without blinding of treatment allocation to their participants (e.g. a sham control) based on the description of individual studies, and (3) Different EEG-NF protocols (e.g., TB or SCP protocols). Mixed-effects meta-regression was used to investigate the impact of continuous variables [e.g., intelligent quotient (IQ)] on the therapeutic effect of EEG-NF on attentional performance among patients with ADHD.. We also performed Q statistics and used the corresponding p values to assess the heterogeneity of ESs.

With respect to the evaluation of publication bias, funnel plots were inspected when there were fewer than 10 datasets [30], while Egger’s tests were performed if there were 10 or more datasets [31]. On encountering funnel plot asymmetry, potentially missing studies were imputed by using the Duval and Tweedie’s trim and fill method [32]. Sensitivity test was conducted with the leave-one-out approach through removing one study each time and repeating the step to estimate the effect of each study on the overall ES [30].

Results

Study characteristics

Figure 1 summarizes the process of identifying eligible studies for the current meta-analysis. Of the 58 full-text articles assessed for eligibility, 44 were excluded for failing to meet the inclusion criteria. The reasons for exclusion are detailed in Additional file 3: Table S3. Finally, fourteen articles using the randomized controlled trial (RCT) design were selected for the current study [22, 33,34,35,36,37,38,39,40,41,42,43,44,45] (Table 1). The Kappa index of agreement was 1.0. The eligible studies and their risk of bias are shown in Fig. 2.

Fig. 1
figure 1

PRISMA diagram of identifying eligible studies

Full size image
Table 1 Summary of characteristics of studies in the current meta-analysis
Full size table
Fig. 2
figure 2

Risks of bias of the included studies. (Asterisk) both authors and studies received no financial support from pharmaceutical companies

Full size image

A total of 718 participants with a mean age of 14.96 years (range, 8.66–37.8) and a female prevalence of 22.41% (range, 0–60.9%) were included. Among them, 429 received surface EEG-NF treatment. Of the 12 studies that used the TB ratio as their main EEG-NF treatment protocol, two also included beta wave enhancement in their protocols (Table 1). On the other hand, two studies used the SCP protocol [42, 43]. In the current meta-analysis, comparison groups consisted of TAU/waitlist and other non-pharmaceutical interventions (Table 1). Two studies used some forms of blinding such as sham control or sham electrode to blind their participants to treatment assignment [22, 38]. Of all the eligible studies, only three conducted a follow-up investigation. The diagnostic criteria for ADHD were mostly based on DSM-IV-TR with children being the main targeted group. Among the eligible studies, eight provided data on the intelligence quotient (IQ) of the testing subjects. Participants were not allowed to receive any stimulant medications in four out of the 14 studies, while all ADHD patients underwent medication treatment in one study by Lee and Jung [36].

Regarding the assessment of attentional performance, various tests were employed across the included studies, namely different versions of continuous performance test (CPT), stop-signal test, d2 test, Stroop task, working memory test, the subtests of Wechsler intelligence test, and attention network test. The number of EEG-NF sessions ranged from 20 to 40. Half of the studies (n = 7) were conducted in Germany, while two were performed in Asia [36, 45].

Quantitative data synthesis

The current meta-analysis of data from fourteen studies found more improvement in attentional performance in ADHD subjects receiving surface EEG-NF than that in their comparators (Hedges’ g = 0.29, p < 0.01) (Fig. 3). The assessment tools used in individual studies for different domains of attentional performance are provided in Additional file 4: Table S4. The ES was strong in the leave-one-out sensitivity analysis (p < 0.01), suggesting that the main result was not driven by any single study. Egger’s test was significant (p = 0.02), implicating a high risk of publication bias.

Fig. 3
figure 3

Forest plot of effect sizes for comparing the difference in the improvement of attentional performance between electroencephalographic (EEG) neurofeedback group and its comparators

Full size image

Our results showed that EEG-NF had a significant beneficial impact on sustained attention (Hedges’ g = 0.32, p < 0.01), whereas its effects on selective attention (i.e., sustained attention plus cognitive control) [22, 34, 37, 38, 41, 44] (Hedges’ g = 0.07, p = 0.57) and working memory (e.g., digit span backwards) [34, 35] (Hedges’ g = 0.10, p = 0.59) were limited. Focusing on different components of sustained attention, there was a significant reduction in omissions (n = 4, Hedges’ g = 0.32, p = 0.03) but not in reaction time (n = 6, Hedges’ g = 0.11, p = 0.42) (Fig. 4a, b). Therefore, the findings suggested that surface EEG-NF could improve a subject’s sustained attention by reducing negligence.

Fig. 4
figure 4

Forest plot of effect sizes for comparing the difference in a omissions and b reaction time between electroencephalographic (EEG) neurofeedback group and its comparators

Full size image

Regarding the sustained effects of EEG-NF, only three studies provided follow-up data for attentional performance 6–12 months post-treatment [22, 34, 35]. A comparison of the results of follow-up assessments between EEG-NF and their comparators did not show a significant difference in the improvement of attentional performance 6 or 12 months after EEG-NF (p = 0.42).

Subgroup analysis and meta-regression

Despite limited data availability, subgroup analyses of the impacts of the intensity of treatment, the quality of blinding, and the types of EEG-NF protocols on the improvement of attentional performance all showed a significant difference (all p < 0.05) (Table 2). In respect of the intensity of treatment, which was defined as the number of NF sessions per week, subgroup analysis revealed that three sessions per week was associated with a significantly higher ES than that with other intensities (i.e., one or two NF sessions per week) of EEG-NF intervention (p = 0.03). Besides, subgroup analysis showed significantly poorer therapeutic outcomes of surface EEG-NF in studies that involved participants who were unaware of treatment allocation compared to those in studies without blinding of treatment allocation to their participants (p = 0.006). Finally, the therapeutic efficacy of EEG-NF in studies that also focused on rewarding to beta activities was superior to that in those adopting only the TB ratio or the SCP protocol (p = 0.02) Using the mixed-effects model, our meta-regression demonstrated a significant negative correlation between the therapeutic benefits of surface EEG-NF and the percentage of females (p = 0.02). Otherwise, there was no significant association between the therapeutic benefits of surface EEG-NF and other continuous variables (Table 3).

Table 2 Subgroup analysis of factors affecting therapeutic benefits of electroencephalographic neurofeedback (EEG-NF) in subjects with attention deficit hyperactivity disorder (ADHD) and their comparators (Hedges’ g)
Full size table
Table 3 Regression coefficients of improvement in attentional performance in included studies using mixed-effects model
Full size table

Discussion

To our best knowledge, the present study is the first meta-analysis to investigate the efficacy of surface EEG-NF for improving different components of attentional performance in patients with ADHD assessed with objective laboratory measures. Although one previous meta-analysis reported that surface EEG-NF was not effective in ADHD patients when laboratory measures of attentional performance were used for the evaluation of treatment outcomes, that study included only six trials and did not address different components of attentional performance [7]. In contrast, the inclusion of more trials (n = 14) in the current meta-analysis enabled the assessment of the therapeutic impacts of EEG-NF on different components of attentional performance as well as the identification of important factors that could influence treatment outcomes through subgroup analysis and meta-regression.

Most of the previous seven meta-analyses of the efficacy of surface EEG-NF in patients with ADHD evaluated treatment outcomes mainly based on behavioral rating with the inclusion of laboratory measures only in one study [5, 7, 9,10,11,12,13]. All studies consistently found that surface EEG-NF was effective for improving inattention in ADHD patients when assessed by most-proximal evaluators, mostly parents, while the results from possibly blind evaluators (i.e., teachers) remained inconsistent [5, 7, 9,10,11,12,13]. The latest meta-analysis of 17 trials demonstrated that surface EEG-NF mainly improved inattention symptoms in ADHD patients when compared with the non-active control groups (i.e., waiting list) but the improvement was still more obvious when assessed by most-proximal evaluators (ES: − 0.33, i.e. parents) than that by possibly blind evaluators (ES: − 0.25, i.e. teacher) [5]. A previous study has also underscored the high susceptibility of the behavioral rating scales to informant bias [20], which could be even more significant when the intervention is not blinded to the participants. Moreover, the possibly blind evaluators may not be blinded because none of the trials actually prevented their participants from informing their evaluators (e.g., teachers) of the treatments that they received. This is partly supported by the only meta-analysis investigating laboratory measures of attentional performance that showed a further reduction in ES from 0.29 to 0.13 when laboratory measures were used for evaluating behavioral outcomes [7].

In contrast, laboratory measures of attentional performance may be less susceptible to informant bias [14] and could theoretically generate more reliable results. Besides, since improvement in neurocognitive functioning (i.e., endophenotype) should theoretically precede behavioral manifestation (i.e., phenotype), assessment of the former with attention tests in the laboratory setting may serve as an earlier indicator of EEG-NF-associated improvement than evaluation of the latter with behavioral rating scales [46]; therefore, laboratory measures are expected to more sensitively and accurately reflect the desired improvements in attentional performance from surface EEG-NF than behavioral changes from parents’ observations. Overall, we found a larger effect size regarding the improvement in attentional performance assessed by laboratory measures than that reported in a previous meta-analysis (Hedges’ g = 0.29 vs. 0.13, respectively) [7]. In contrast to behavioral rating scales, our meta-analysis provided important evidence from a different perspective.

Our subgroup analysis also demonstrated a significantly poorer therapeutic effect of surface EEG-NF on attentional performance in ADHD patients when participants were blinded to their treatment assignments (Hedges’ g = 0.05, p = 0.006). Apart from observation bias in favor of surface EEG-NF, a lack of blinding to participants may also lead to an enhancement of attentional performance due to increased motivation, which is commonly known as Hawthorne effect. Previous evidence showed that placebo effects, confidence in technology and therapeutic alliance along with other therapeutic factors could all contribute to therapeutic effects of EEG-NF through increasing motivation [47,48,49]. On the other hand, expectancy of self-efficacy, which is a crucial element of neurofeedback interventions [50], may be hampered in placebo-controlled intervention studies. Because only two studies blinded their participants to treatment allocation in our subgroup analysis, the finding regarding the effect of blinding of treatment allocation to participants on their improvement in attentional function needs to be interpreted with caution.

Our subgroup analysis focusing on different components of attentional performance further demonstrated a significant positive impact of surface EEG-NF only on sustained attention without significant effects on selective attention and working memory. Although motivation is important for both sustained attention and working memory [51, 52], the latter may require training involving multi-domains or more specific areas of the brain because it is generally accepted to be part of higher-order cognitive abilities [53]. Together with the finding that surface EEG-NF was considerably less effective when participant were blinded to treatment allocation and when laboratory measures for assessment were adopted, our results suggest that both motivation and confidence in surface EEG-NF may be important factors for the observed effectiveness and surface EEG-NF could only improve sustained attention which is considered a more basic attentional function [54].

Our subgroup analysis revealed that surface EEG-NF protocols including beta wave enhancement were more effective than those using SCP modulation or those focusing only on the TB ratio, indicating that the inclusion of beta wave enhancement was a better strategy. Evidence has shown that suppressed beta activities correlate with inattention and hyperactive symptoms, while increased theta activities are associated with impulsive behaviors [55]. However, the purely negative effect of theta activity on attention remains controversial as it is also associated with insight and creativity [6]. Therefore, the rationale for reducing the TB ratio to improve attentional performance may not be well justified. Additionally, our result was supported by another study that reported no significant difference in theta activities between children with ADHD and their normal developing counterparts, although ADHD children with a slow reaction time were found to have lower beta activities than those in normal developing children [19]. Therefore, consistent with the result of that study [19], our finding supports that enhancing beta activity may be a more effective target of EEG-NF than reducing the TB ratio for ADHD patients. Nevertheless, of the 14 studies included in the current meta-analysis, 12 investigated protocols addressing the TB ratio with only two also including beta wave enhancement. Similarly, although the ES of trials using the SCP protocol was slightly larger than that in those adopting the TB protocol, only two studies used the SCP protocol in our subgroup analysis. Despite the preliminary nature of the finding, our results may provide a direction for further investigations into the efficacy of different EEG-NF protocols for attentional training in patients with ADHD.

Our analysis on the sustained effects of EEG-NF at 6–12 months post-treatment failed to show significant difference in attentional performance between the EEG-NF and comparison groups, although this finding was derived from only three available trials [22, 34, 35]. In contrast, a previous meta-analysis demonstrated sustained treatment effects of surface EEG-NF for at least 6 months [9], while evidence was mixed from other reports [22, 34, 56, 57]. Because we could only include three trials for analyzing the long-term effects (i.e., over 6 months), our results need to be interpreted with caution. Nevertheless, given the potential concerns about the cost-effectiveness of EEG-NF [6], the sustainability of its therapeutic benefits and the need for boosting sessions in the long run remain important questions to guide clinical decision for both clinicians and caregivers (e.g., patients), further studies are warranted to address this issue.

In the present study, subgroup analysis focusing on different intensities of treatment suggested that participants with NF training for at least three sessions per week achieved significantly better improvement in sustained attention than those with a lower treatment intensity. A shorter interval between training sessions may be associated with a more significant increase in synaptic efficacy according to the concept of neuroplasticity in a process called Hebbian plasticity [58]. Although the ES of EEG-NF with one session per week was higher than that in programs offering two sessions per week, inclusion of one large-scale study choosing the intensity of two sessions per week with a sham condition [22] may lead to a lower efficacy of EEG-NF. In contrast, the ES of trials with three sessions per week remained the largest despite the inclusion of one study using sham electrodes [38], suggesting that EEG-NF with more frequent training may achieve better therapeutic outcomes.

In addition, our meta-regression analysis found that female gender correlated with poorer therapeutic effects of EEG-NF. This result needs to be judiciously interpreted as most trials enrolled far more males than females. In particular, of the two trials that did not recruit female participants, one focused on beta enhancement and showed a very large effect size [45]. Therefore, the choice of treatment protocol, rather than gender, may contribute to the difference in treatment efficacy. Consistently, a previous trial including a more balanced gender profile failed to identify a significant influence of gender on neurofeedback learning [59]. The current study demonstrated that other demographic factors, medication use, and subtypes of ADHD were not associated with the efficacy of surface EEG-NF.

Despite the strength of the current study as the first meta-analysis to explore the therapeutic impact of surface EEG-NF on different components of attentional performance in patients with ADHD evaluated with objective laboratory measures, there were several limitations that need to be considered for accurate interpretations of its findings. First, although we were able to include more trials compared with a previous meta-analysis [7], the number of trials (14 RCTs) and participants (n = 718) were still too small to provide tangible evidence. In particular, our preliminary finding on the long-term therapeutic effects of EEG-NF, which was derived from only three trials that provided information about outcomes at 6–12 months follow-ups [22, 34, 35], could not preclude the potential benefits of prolonged EEG-NF treatment. Second, most studies had poor methodological qualities regarding performance bias and detection bias. However, our study used more objective measures of attentional performance in a laboratory-based setting, which is another major strength of this meta-analysis. Third, the heterogeneity of the included studies, including differences in the types of EEG-NF protocols, the control conditions from no active treatment to medications, and the recruited age groups, may limit generalization of our results. Nevertheless, we performed subgroup analysis and meta-regression to address those specific issues. Fourth, our findings only supported significant improvements in attention outcomes associated with surface EEG-NF intervention based on continuous variables, but were unable to provide clear information regarding the clinical significance when using parameters such as number needed to treat (NNT). Finally, taking into account the significant publication bias shown in the present meta-analysis and the lack of blinding in most studies as well as the very small effect size, the true effect of surface EEG NF on attentional performance needs to be interpreted with the utmost discretion.

Conclusion

In general, focusing on laboratory measures, our results supported the use of surface EEG-NF for improving attentional performance through the modulation of basic neurocognitive functioning in patients with ADHD. The current study suggested that surface EEG-NF with beta wave enhancement may be a more effective protocol for improving sustained attention through reducing omissions but this effect failed to sustain a follow-up period longer than 6 months. However, given the small number of trials and the poor methodological qualities regarding blinding, our findings need to be judiciously interpreted and warrant further investigations for validation.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ADHD:

Attention-deficit/hyperactivity disorder

CMA:

Comprehensive Meta-Analysis version

CPT:

Continuous performance test

DSM-IV-TR:

The Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision

EEG-NF:

Electroencephalographic neurofeedback

ES:

Effect size

fMRI:

Functional magnetic resonance imaging

Fz:

Frontal

IQ:

Intelligent quotient

LC:

Locus coeruleus

NNT:

Number needed to treat

RCT:

Randomized control trial

SCP:

Slow cortical potential

TAU:

Treatment as usual

TB:

Theta/beta

References

  1. Wolraich, M.L., J.F. Hagan, Jr., C. Allan, et al., Clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Pediatrics. 2019;144(4).

  2. Correll CU, Cortese S, Croatto G, et al. Efficacy and acceptability of pharmacological, psychosocial, and brain stimulation interventions in children and adolescents with mental disorders: an umbrella review. World Psychiatry. 2021;20(2):244–75.

    Article  Google Scholar 

  3. Searight HR, Robertson K, Smith T, et al. Complementary and alternative therapies for pediatric attention deficit hyperactivity disorder: a descriptive review. ISRN Psychiatry. 2012;2012: 804127.

    Article  Google Scholar 

  4. Monastra VJ, Monastra DM, George S. The effects of stimulant therapy, EEG biofeedback, and parenting style on the primary symptoms of attention-deficit/hyperactivity disorder. Appl Psychophysiol Biofeedback. 2002;27(4):231–49.

    Article  Google Scholar 

  5. Riesco-Matías P, Yela-Bernabé JR, Crego A, et al. What do meta-analyses have to say about the efficacy of neurofeedback applied to children with ADHD? Review of previous meta-analyses and a new meta-analysis. J Atten Disord. 2021;25(4):473–85.

    Article  Google Scholar 

  6. Marzbani H, Marateb HR, Mansourian M. Neurofeedback: a comprehensive review on system design, methodology and clinical applications. Basic Clin Neurosci. 2016;7(2):143–58.

    Google Scholar 

  7. Cortese S, Ferrin M, Brandeis D, et al. Neurofeedback for attention-deficit/hyperactivity disorder: meta-analysis of clinical and neuropsychological outcomes from randomized controlled trials. J Am Acad Child Adolesc Psychiatry. 2016;55(6):444–55.

    Article  Google Scholar 

  8. Gualtieri CT. A practical approach to objective attention deficit/hyperactivity disorder diagnosis and management. Psychiatry (Edgmont). 2005;2(7):16–25.

    Google Scholar 

  9. Van Doren J, Arns M, Heinrich H, et al. Sustained effects of neurofeedback in ADHD: a systematic review and meta-analysis. Eur Child Adolesc Psychiatry. 2019;28(3):293–305.

    Article  Google Scholar 

  10. Arns M, de Ridder S, Strehl U, et al. Efficacy of neurofeedback treatment in ADHD: the effects on inattention, impulsivity and hyperactivity: a meta-analysis. Clin EEG Neurosci. 2009;40(3):180–9.

    Article  Google Scholar 

  11. Sonuga-Barke EJ, Brandeis D, Cortese S, et al. Nonpharmacological interventions for ADHD: systematic review and meta-analyses of randomized controlled trials of dietary and psychological treatments. Am J Psychiatry. 2013;170(3):275–89.

    Article  Google Scholar 

  12. Micoulaud-Franchi JA, Geoffroy PA, Fond G, et al. EEG neurofeedback treatments in children with ADHD: an updated meta-analysis of randomized controlled trials. Front Hum Neurosci. 2014;8:906.

    Article  Google Scholar 

  13. Hodgson K, Hutchinson AD, Denson L. Nonpharmacological treatments for ADHD: a meta-analytic review. J Atten Disord. 2014;18(4):275–82.

    Article  Google Scholar 

  14. Berger I, Slobodin O, Cassuto H. Usefulness and validity of continuous performance tests in the diagnosis of attention-deficit hyperactivity disorder children. Arch Clin Neuropsychol. 2017;32(1):81–93.

    Google Scholar 

  15. Awh E, Vogel EK, Oh SH. Interactions between attention and working memory. Neuroscience. 2006;139(1):201–8.

    Article  CAS  Google Scholar 

  16. Vernon DJ. Can neurofeedback training enhance performance? An evaluation of the evidence with implications for future research. Appl Psychophysiol Biofeedback. 2005;30(4):347–64.

    Article  Google Scholar 

  17. Shereena EA, Gupta RK, Bennett CN, et al. EEG neurofeedback training in children with attention deficit/hyperactivity disorder: a cognitive and behavioral outcome study. Clin EEG Neurosci. 2019;50(4):242–55.

    Article  CAS  Google Scholar 

  18. Leins U, Goth G, Hinterberger T, et al. Neurofeedback for children with ADHD: a comparison of SCP and Theta/Beta protocols. Appl Psychophysiol Biofeedback. 2007;32(2):73–88.

    Article  Google Scholar 

  19. Liao YC, Guo NW, Su BY, et al. Frontal beta activity in the meta-intention of children with attention deficit hyperactivity disorder. Clin EEG Neurosci. 2021;52(2):136–43.

    Article  Google Scholar 

  20. Skounti M, Philalithis A, Galanakis E. Variations in prevalence of attention deficit hyperactivity disorder worldwide. Eur J Pediatr. 2007;166(2):117–23.

    Article  Google Scholar 

  21. Group NC. Double-blind placebo-controlled randomized clinical trial of neurofeedback for attention-deficit/hyperactivity disorder with 13-month follow-up. J Am Acad Child Adolesc Psychiatry. 2021;60(7):841–55.

    Article  Google Scholar 

  22. Schönenberg M, Wiedemann E, Schneidt A, et al. Neurofeedback, sham neurofeedback, and cognitive-behavioural group therapy in adults with attention-deficit hyperactivity disorder: a triple-blind, randomised, controlled trial. Lancet Psychiatry. 2017;4(9):673–84.

    Article  Google Scholar 

  23. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol. 2009;62(10):1006–12.

    Article  Google Scholar 

  24. Moher D, Shamseer L, Clarke M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4:1.

    Article  Google Scholar 

  25. McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb). 2012;22(3):276–82.

    Article  Google Scholar 

  26. Higgins JP, Thomas J, Chandler J, et al. Cochrane handbook for systematic reviews of interventions. New York: Wiley; 2019.

    Book  Google Scholar 

  27. Cohen J, editor. Statistical power analysis for the behavioral sciences. New York: Routledge Academic; 1988.

    Google Scholar 

  28. Borenstein M, Hedges LV, Higgins JP, et al. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods. 2010;1(2):97–111.

    Article  Google Scholar 

  29. Riley RD, Higgins JP, Deeks JJ. Interpretation of random effects meta-analyses. BMJ. 2011;342: d549.

    Article  Google Scholar 

  30. Higgins J, Green S, editors. Cochrane handbook for systematic reviews of interventions. Version 5.0.2. ed. 2009, The Cochrane Collaboration.

  31. Egger M, Davey Smith G, Schneider M, et al. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34.

    Article  CAS  Google Scholar 

  32. Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56(2):455–63.

    Article  CAS  Google Scholar 

  33. Bakhshayesh AR, Hänsch S, Wyschkon A, et al. Neurofeedback in ADHD: a single-blind randomized controlled trial. Eur Child Adolesc Psychiatry. 2011;20(9):481–91.

    Article  Google Scholar 

  34. Bink M, Bongers IL, Popma A, et al. 1-year follow-up of neurofeedback treatment in adolescents with attention-deficit hyperactivity disorder: randomised controlled trial. BJPsych Open. 2016;2(2):107–15.

    Article  Google Scholar 

  35. Geladé K, Janssen TWP, Bink M, et al. A 6-month follow-up of an RCT on behavioral and neurocognitive effects of neurofeedback in children with ADHD. Eur Child Adolesc Psychiatry. 2018;27(5):581–93.

    Article  Google Scholar 

  36. Lee EJ, Jung CH. Additive effects of neurofeedback on the treatment of ADHD: a randomized controlled study. Asian J Psychiatr. 2017;25:16–21.

    Article  Google Scholar 

  37. Lévesque J, Beauregard M, Mensour B. Effect of neurofeedback training on the neural substrates of selective attention in children with attention-deficit/hyperactivity disorder: a functional magnetic resonance imaging study. Neurosci Lett. 2006;394(3):216–21.

    Article  Google Scholar 

  38. Maurizio S, Liechti MD, Heinrich H, et al. Comparing tomographic EEG neurofeedback and EMG biofeedback in children with attention-deficit/hyperactivity disorder. Biol Psychol. 2014;95:31–44.

    Article  Google Scholar 

  39. Moreno-García I, Meneres-Sancho S, Camacho-Vara de Rey C, et al. A Randomized controlled trial to examine the posttreatment efficacy of neurofeedback, behavior therapy, and pharmacology on ADHD measures. J Atten Disord. 2019;23(4):374–83.

    Article  Google Scholar 

  40. Steiner NJ, Sheldrick RC, Gotthelf D, et al. Computer-based attention training in the schools for children with attention deficit/hyperactivity disorder: a preliminary trial. Clin Pediatr (Phila). 2011;50(7):615–22.

    Article  Google Scholar 

  41. Wangler S, Gevensleben H, Albrecht B, et al. Neurofeedback in children with ADHD: specific event-related potential findings of a randomized controlled trial. Clin Neurophysiol. 2011;122(5):942–50.

    Article  Google Scholar 

  42. Holtmann M, Stadler C, Zepf F et al. Specific effects of neurofeedback on impulsivity in ADHD: evidence from a prospective randomized pilot study. 2007; LIX–LIX.

  43. Heinrich H, Gevensleben H, Freisleder FJ, et al. Training of slow cortical potentials in attention-deficit/hyperactivity disorder: evidence for positive behavioral and neurophysiological effects. Biol Psychiatry. 2004;55(7):772–5.

    Article  Google Scholar 

  44. Barth B, Mayer-Carius K, Strehl U, et al. A randomized-controlled neurofeedback trial in adult attention-deficit/hyperactivity disorder. Sci Rep. 2021;11(1):16873.

    Article  CAS  Google Scholar 

  45. Rajabi S, Pakize A, Moradi N. Effect of combined neurofeedback and game-based cognitive training on the treatment of ADHD: a randomized controlled study. Appl Neuropsychol Child. 2020;9(3):193–205.

    Article  Google Scholar 

  46. Holtmann M, Sonuga-Barke E, Cortese S, et al. Neurofeedback for ADHD: a review of current evidence. Child Adolesc Psychiatr Clin N Am. 2014;23(4):789–806.

    Article  Google Scholar 

  47. Thibault RT, Lifshitz M, Raz A. The self-regulating brain and neurofeedback: experimental science and clinical promise. Cortex. 2016;74:247–61.

    Article  Google Scholar 

  48. Heywood C, Beale I. EEG biofeedback vs. placebo treatment for attention-deficit/hyperactivity disorder: a pilot study. J Atten Disord. 2003;7(1):43–55.

    Article  CAS  Google Scholar 

  49. Baydala L, Wikman E. The efficacy of neurofeedback in the management of children with attention deficit/hyperactivity disorder. Paediatr Child Health. 2001;6(7):451–5.

    Article  CAS  Google Scholar 

  50. Harris S. An investigation of the effects of neurofeedback training on attention deficit-hyperactivity disorder (ADHD) symptoms, depression, anxiety, and academic self-efficacy in college students. 2017.

  51. Szatkowska I, Bogorodzki P, Wolak T, et al. The effect of motivation on working memory: an fMRI and SEM study. Neurobiol Learn Mem. 2008;90(2):475–8.

    Article  Google Scholar 

  52. Smith AB, Halari R, Giampetro V, et al. Developmental effects of reward on sustained attention networks. Neuroimage. 2011;56(3):1693–704.

    Article  Google Scholar 

  53. Johnson MK, McMahon RP, Robinson BM, et al. The relationship between working memory capacity and broad measures of cognitive ability in healthy adults and people with schizophrenia. Neuropsychology. 2013;27(2):220–9.

    Article  Google Scholar 

  54. Sarter M, Givens B, Bruno JP. The cognitive neuroscience of sustained attention: where top-down meets bottom-up. Brain Res Brain Res Rev. 2001;35(2):146–60.

    Article  CAS  Google Scholar 

  55. Bresnahan SM, Anderson JW, Barry RJ. Age-related changes in quantitative EEG in attention-deficit/hyperactivity disorder. Biol Psychiatry. 1999;46(12):1690–7.

    Article  CAS  Google Scholar 

  56. Gevensleben H, Holl B, Albrecht B, et al. Neurofeedback training in children with ADHD: 6-month follow-up of a randomised controlled trial. Eur Child Adolesc Psychiatry. 2010;19(9):715–24.

    Article  Google Scholar 

  57. Steiner NJ, Frenette EC, Rene KM, et al. In-school neurofeedback training for ADHD: sustained improvements from a randomized control trial. Pediatrics. 2014;133(3):483–92.

    Article  Google Scholar 

  58. Ros T, Baars BJ, Lanius RA, et al. Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework. Front Human Neurosci. 2014;8:1008–1008.

    Article  Google Scholar 

  59. Zuberer A, Minder F, Brandeis D, et al. Mixed-effects modeling of neurofeedback self-regulation performance: moderators for learning in children with ADHD. Neural Plast. 2018;2018:1–5.

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This research received no specific grant from any funding agency, commercial or not-for-profit sectors.

Author information

Author notes

  1. Hsien‐Jane Chiu and Cheuk-Kwan Sun contributed equally as first authors.

  2. Weilun Chung and Pin-Yang Yeh contributed equally as corresponding authors.

Authors and Affiliations

  1. Taoyuan Psychiatric Center, Ministry of Health and Welfare, Taoyuan, Taiwan

    Hsien‐Jane Chiu

  2. Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Hsien‐Jane Chiu

  3. Department of Emergency Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan

    Cheuk-Kwan Sun

  4. School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan

    Cheuk-Kwan Sun

  5. Department of Psychiatry, Tsyr-Huey Mental Hospital, Kaohsiung Jen-Ai’s Home, No. 509, Fengping 1St Rd., Daliao Dist, Kaohsiung, Taiwan

    Hsin-Yi Fan, Yu-Shian Cheng & Weilun Chung

  6. Department of Psychiatry, Mackay Memorial Hospital, Taipei, Taiwan

    Ruu‐Fen Tzang

  7. Department of Psychiatry, China Medical University Hsinchu Hospital, China Medical University, Hsinchu, Taiwan

    Ming-Yu Wang & Ying-Chih Cheng

  8. Department of Health Services Administration, China Medical University, Taichung, Taiwan

    Ming-Yu Wang

  9. Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan

    Ying-Chih Cheng

  10. Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan

    Ying-Chih Cheng

  11. Department of Psychology, College of Medical and Health Science, Asia University, 500, Lioufeng Rd., Wufeng, Taichung, 41354, Taiwan

    Pin-Yang Yeh

Authors
  1. Hsien‐Jane Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cheuk-Kwan Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hsin-Yi Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ruu‐Fen Tzang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ming-Yu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ying-Chih Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yu-Shian Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Pin-Yang Yeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Weilun Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

WC and YSC conceived and designed the study, HYF, RFT, and MYW contributed to data extraction, YCC and PYY analyzed the data, HJC and CKS were major contributors to writing the manuscript. All authors contributed sufficiently to this work. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yu-Shian Cheng, Pin-Yang Yeh or Weilun Chung.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors agreed on the publication of our article in Child and Adolescent Psychiatry and Mental Health.

Competing interests

The authors declare that there is no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Applied keyword and the search result in each database.

Additional file 2: Table S2.

PRISMA checklist of the present meta-analysis.

Additional file 3: Table S3.

Reasons for study exclusion.

Additional file 4:Table S4.

Components of attention tests targeting different cognitive domains in the included studies.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chiu, H., Sun, CK., Fan, HY. et al. Surface electroencephalographic neurofeedback improves sustained attention in ADHD: a meta-analysis of randomized controlled trials. Child Adolesc Psychiatry Ment Health 16, 104 (2022). https://doi.org/10.1186/s13034-022-00543-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13034-022-00543-1

Keywords

Child and Adolescent Psychiatry and Mental Health

ISSN: 1753-2000