Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) describes a lifelong developmental condition, which among other things, impacts on the way an individual may relate to their environment and interact with other people. Symptoms associated with ASD typically present early in childhood development. As a spectrum disorder, the range of difficulties and the degree to which an individual might experience them vary along a continuum. For instance, some individuals may be able to sufficiently engage in activities of daily functioning and lead relatively typical lives, whereas other individuals may experience considerably more difficulty with daily functioning and encounter additional challenges with learning and socialising, and as such may require continued assistance and specialist support. The main areas in which individuals experience difficulties are social communication, social interaction, and restricted or repetitive behaviours and interests.

What does Neurofeedback improve?

Imagine an old fashioned TV set; boxy convex glass screen, shiny silver antenna. If after turning on the TV we get static occasionally, usually it can be ignored or we’d try switching the TV on and off. However, if every time we want to watch our favourite channel, we get static resulting in both grainy sound and picture, it’s likely to be a frustrating experience until we book a repair. Similarly, neurofeedback addresses specific areas of dysregulation by reducing the ‘static’ of the brain, and clarifying the picture. Think of it as a tune-up for the brain. In relation to ASD, neurofeedback consistently demonstrates that it can address core deficits of a disorder, improving physical and emotional ability to self-soothe while improving the ability to self-regulate sensory input.

Sensory input from external stimuli (e.g., one’s environment) reach the spinal cord and ultimately the brain (central nervous system) through nerves attached to the eyes, the nose, mouth/tongue, ears, and skin (peripheral nervous systems), which relay information onto specific brain regions and associated key networks responsible for interpreting sight, sound, touch, taste, and smell. Neurofeedback aims to fine-tune the brain by dampening excessive sensory input and activity (the ‘static’) in the specific brain areas discussed in detail below:

Training the right side of the brain:

  • Hyperactive behaviour can be reduced and sensory integration (increased understanding of the world and increased social awareness), bodily calming, and body awareness may be enhanced.

Training on the right front:

  • Rages, emotional outbursts, and overall emotional expression (e.g., appropriate emotional communication) can be improved.

Training on the left front side:

  • Changes can be made in attention and obsessive-compulsive symptoms.

Neurofeedback therapy (NFT) provides an interesting and feasible treatment option to, arguably, one of the most difficult disorders to treat. Using a series of fun computerised exercises to teach individuals to gain better control over their own brain activity, one is able to induce significant improvements in their own emotional stability, and is shown to be effective in both children and adults alike. NFT systematically addresses the dysfunctional neural connections in the brain and thus encourages new and stronger networks to form and flourish.


Supporting Research

The following supporting research summarises a selection of studies relating to ASD and NFT, highlighting the magnitude of the benefits experienced by individuals suffering from the disorder as measured in independent studies using functional MRI (fMRI) testing and other visualisation techniques.

Datko, Pineda and Müller (2018) studied the effects of sensorimotor mu-rhythm-based neurofeedback training (NFT) on imitation-related brain activation in children with high-functioning Autism Spectrum Disorders (ASD) and a matched control group of typically developing children (aged between 8-17). In line with past research, children with ASD in the present study exhibited atypical reductions in activation of imitation and action-observation brain areas compared with typically developing children. The present study aimed to improve activation in these areas. Results showed that following NFT, ASD participants showed increased activation in the brain’s mirror neuron networks during imitation and action-observation tasks, and that these were accompanied by neurophysiological changes in those regions. Social behaviours are affected by reductions in mirror neuron networks. Thus, improving activation in those networks in children with ASD can promote more positive social behaviours.

In 2016, Yao Wang and colleagues found a linear decrease of theta/beta ratio and a linear increase of relative power of gamma activity over 18 weekly sessions of neurofeedback in 18 high functioning children with  high functioning autism. Theta/beta ratio refers to specific brain activity as shown by two types of waves. Theta waves often arise during REM sleep,  hypnosis and usually indicate an extreme state of relaxation (Wang & colleagues, 2016). Beta waves are associated with normal wakefulness and consciousness (Wang et al, 2016). This ratio of theta/beta waves is often interpreted as a bio-marker for attentional control (Putman & Colleagues, 2014). Thus a decrease in the ratio, suggests that there are more beta waves produced than theta, indicative of increased concentration versus daydreaming.

Additionally, high gamma activity seems to be suggestive of  greater conscious awareness (Singer & Grey, 2016). Thus, if we look at the theta/beta ratio in combination with gamma activity we can gain a clearer picture as to how neurofeedback improves attentional and emotional control in ASD individuals. Results from the study, showed statistically significant differences in performance in lethargy and social areas. It seems neurofeedback was dampening down the overactive signals caused by the inherent functional deficits in the brain, allowing them to maintain emotional stability and interact with others more easily than before; significantly impacting their quality of life on an individual level and extending to their carers, guardians and family.

In 2015,  Dr. Franziska Eller, a research scientist at the University of Aarhus, found that individualised Neurofeedback training in children with ASD led to improvements in several domains, including positive changes in brain wave activity, behavioural aspects and imitation abilities; consistent with Wang and colleagues (2016) study as mentioned above. The behavioural testing conducted within the study included assessment on two autism questionnaires and an imitation test (social behaviour). Results concluded the treatment group experienced advances in behaviour compared to control and suggests neurofeedback has useful application as an incredibly effective adjunct treatment to the current pure behaviour therapy regimen of ASD treatment.

adhd readingIn 2015, Garcia-Bejillo and colleagues conducted a systematic review of 17 empirical studies on neurofeedback and how it affects core ASD symptoms. In reviewing the literature, the researchers found that neurofeedback is highly specific and efficacious in the treatment of abnormal EEG patterns and core ASD symptoms such as impairments in attention and cognitive functions, anxiety or behavioural disorders. She created set up where EEG training aimed at the somatosensory cortex (the area responsible for primary sensory input) would be able to ‘tune down’ this static and get activity to more stable and functional levels. In applying this method of neurofeedback training, children with autism spectrum disorder showed significant improvements in brain activity, emotional responsiveness (better emotion recognition and spontaneous imitation) and behaviour in everyday life (Fredrich & Colleagues, 2015). Similarly,  multiple studies conducted by Zivoder and colleagues (2015) supported these findings, seeing positive changes in; cognitive flexibility, fluency of speech and language; behaviour, (less aggressive, more cooperation, and better communication), attention span and sensory motor skills; sleep quality and time; largely attributable to neurofeedback therapy (Jarusziewicz et al., 2002; Coben & Padolosky, 2007; Kouijzer et al, 2010; Haddaddi et al, 2011; Karimi et al, 2011; Kouijzer et al., 2013).

Interestingly, while most of this research attests to changes in ASD children on brain activity, behavioural and social levels, Pineda et al (2014) showed neurofeedback training led to improvements in children with ASD but not in typically-developing children. This idea is especially peculiar given the children both with and without ASD have been shown to benefit from neurofeedback training, however, previous research has not demarcated a mechanism as to why, how or where these benefits could differ between these groups. Pineda et al (2014) suggest induction of neuroplastic changes via neurofeedback treatment normalises dysfunctional mirroring networks in children with ASD, but typically developing children experience overall reduced synchronization of miu rhythms that leads to decreased social behaviours.

While on the surface, these findings may seem contradictory, in actuality it may be that the electrical patterns in the normal brain are inversely affected by miu suppression, such that suppressing the normal waves can cause ‘static’ and de-concentration.

In the ASD brain, this miu suppression technique, a way to dampen down electrical signals in the brain, could induce greater regulation, producing better behavioural and affective outcomes. Billeci et al (2013), publishing in the renowned open access journal, Human Frontiers of Neuroscience, explored applications of quantitative EEG in identifying and characterising an autistic brain in their meta analytic review. While there was inconsistency within the literature, most studies attested to the theoretical idea of “local over connectivity and global under-connectivity in ASD” as contributing to symptoms. Locally dominant cortical processes were reflected in theta wave activity, and global activity was more correlated connectivity between deep cortical fibres and alpha wave oscillation. While still in its infancy, tailored interventions to ASD using QEEG (Quantitative Electroencephalography or Neurofeedback) technology is a likely a future key therapeutic tool. As shown in Figures 1 to 3 below, connectivity between cortical areas in ASD spectrum disorders is especially interesting when visually represented.


Figure 1. Miu Rhythm Complex derived from Coben et al. (2009)

Figure 1.  QEEG graphs displaying Miu activity in ASD spectrum individuals, showing deficits in mirrored responding and imitation tasks. Sourced from Coben et al. (2009)



Figure 2.  QEEG graphs displaying Orbitofrontal hypoconnectivity in ASD spectrum individuals, accounting for issues with executive planning and social cognition. Sourced from Coben et al. (2009)




Figure 3.  QEEG graphs displaying Right Posterior Hypoconnectivity in ASD spectrum individuals, associated with excess theta activity  (daydreaming) with low conductivity in delta and theta and beta bands as well.Sourced from Coben et al. (2009)

Social interaction and tasks such as imitation, act as stumbling blocks for ASD patients. Studies such as Mathewson et al., (2012) explore the role of alpha power in dysfunctional cortical activity and how it contributes to social symptoms of the disorder. Findings correlated alpha wave activity with behavioural measures showed QEEG at rest was influenced by social skills training. The basis for these explorations into the role of EEG in characterising the disorder were examined by Burnette at al., (2011) in a meta-analysis. Research suggests high functioning ASD children with asymmetry of the left frontolateral lobe and inherently lower IQs displayed weaker scores on social interaction testing compared to similarly low IQ, high functioning children with the same frontolateral asymmetry localised to the right side. Anterior EEG asymmetry in children with ASD may be used as a diagnostic tool to identify anxiety based or obsession based subgroups, which can be indicative of severity and developmental course of autism.

Coben (2009)  and colleagues found EEG abnormalities functionally specific to ASD spectrum individuals:

  1. Hyper-connectivity – mainly between frontotemporal regions and associated with problems with attention, self-regulatory functions, social behavior, and communication skills. social cognition
  2. Miu Rhythm Complex over the sensorimotor strip thought to be associated with reduced integrity of the mirror neuron system that is essential in observing others as a basis for proper responding.
  3. Hypoconnectivity between cortical regions; problems in language processing; increased connectivity in the delta band of frequencies in frontotemporal regions

More fundamentally, Bernier, Dawson, Webb & Murias (2007) explored the likelihood of a causal link between EEG miu rhythm suppression and imitation impairments in high functioning ASD adults. The study consisted of age and IQ matched participants. On the EEG task, the normal attenuation of the miu rhythm was observed by both control and ASD patients. By contrast, observation tasks were significantly more difficult for the ASD individuals, showing limited miu attenuation; culminating in poor imitation ability. While further research is building, it seems miu rhythm dysregulation can contribute to social issues with imitation characterised by ASD.

Coben (2007) investigated the positive effects of neurofeedback on neuronal metabolism and overall long term brain function. While behavioural therapy is a staple for treatment of inattentive, impulsive and hyperactive symptoms, usually years of treatment are required for results to persist. By contrast, neurofeedback therapy yields results within several months of training sessions with therapeutic outcomes maintained and not reversed as is evident with supplementary therapy such as diet and pharmacotherapy. Following forty neurofeedback sessions, approximately 70% of participants displayed decreases in delta wave activity, related to day dreaming behaviour and increases in beta activity, associated with purposeful concentration. This translated to improved performance in the treatment group compared to control in higher level skills of attention, executive functioning, cognitive skills and planning.

Chan, Sze & Cheung (2007) and studies conducted by Chan & Leung (2006) adopted a more longitudinal approach to understanding these brain activity changes in ASD. Chan, Sze & Cheung (2007) detail in their abstract, “Five-minute QEEG data were obtained from 90 normal controls (NCs) and 66 children with ASD. Spectrum analyses revealed that ASD children showed significantly less relative alpha and more relative delta than NC. Specifically, 26% of ASD children and 2% of NCs showed 1.5 SDs of relative alpha below the normative mean. Children with this QEEG profile had 17 times the risk of having ASD than those without such a profile. Sensitivity and specificity of relative alpha were 91% and 73%, respectively. Split-half cross-validation yielded a sensitivity of 76%. Extending this to see if QEEG measured changes affected”.

Cantor, Thatcher, Hrybyk and Kay (1984) established the precedent for applying computerised EEG techniques to analyse and characterise the autistic brain in childhood. Addressing previous study issues with a lack of standardisation and sampling bias, Cantor et al (1984)  conducted experiments matching EEG data from low functioning ASD patients to normal children of the same age. Cortical patterns within ASD children showed greater slow wave activity with less alpha, comparable to EEG features of toddlers. These findings added weight to the theory that maturational lag was a result of underdeveloped cortical tissue in ASD patients.

In looking at the evidence available it seems clear that Autism Spectrum Disordered individuals, particularly children can enjoy substantial reduction in stress, improvements in mood and social skills due to neurofeedback therapy, which acts as a catalyst enabling them to maximise their developmental potential to the best of their ability. For ease of research, the papers analysed and discussed in this review article are below for your perousal.


Attwood T (1998) Asperger’s syndrome: a guide for parents and professionals. Jessica Kingsley, London

Bernier, R., Dawson, G., Webb, S., & Murias, M. (2007). EEG mu rhythm and imitation impairments in individuals with autism spectrum disorder. Brain and cognition, 64(3), 228-237.

Billeci, L., Sicca, F., Maharatna, K., Apicella, F., Narzisi, A., Campatelli, G., Calderoni, S., Pioggia, G. and Muratori, F. (2013). On the application of quantitative EEG for characterizing autistic brain: a systematic review.

Burnette, C. P., Henderson, H. A., Inge, A. P., Zahka, N. E., Schwartz, C. B., & Mundy, P. C. (2011). Anterior EEG asymmetry and the modifier model of autism. Journal of autism and developmental disorders, 41(8), 1113-1124.

Cantor, D. S., Thatcher, R. W., Hrybyk, M., & Kaye, H. (1986). Computerized EEG analyses of autistic children. Journal of autism and developmental disorders, 16(2), 169-187.

Chan, A. S., Sze, S. L., & Cheung, M. C. (2007). Quantitative electroencephalographic profiles for children with autistic spectrum disorder. Neuropsychology, 21(1), 74.

Coben, R. (2007). Autistic spectrum disorder: A controlled study of EEG coherence training targeting social skill deficits. In 15th annual conference of the international society for neurofeedback and research, San Diego, California.

Coben, R., & Padolsky, I. (2007). Assessment-guided neurofeedback for autism spectrum disorder. Journal of Neurotherapy, 11(1), 5-23.

Coben, R. (2009). Efficacy of connectivity guided neurofeedback for autistic spectrum disorder: Controlled analysis of 75 cases with a 1 to 2 year follow-up. Journal of Neurotherapy, 13(1), 81.

Datko, M., Pineda, J. A., & Müller, R. (2018). Positive effects of neurofeedback on autism symptoms correlate with brain activation during imitation and observation. European Journal of Neuroscience, 47, 579-591. doi:10.1111/ejn.13551

Eller, F. (2015). The effectiveness of neurofeedback training for children with autism spectrum disorders. Springer.

Friedrich, E., Sivanathan, A., Lim, T., …., Pillen, S., & Pineda, J. (2015). An effective neurofeedback intervention to improve social interactions in children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 45(1), 4084-4100.

Garcia-Berjillos, E., Alino, M., Gaea, M., Espert, R., & Salvador, A. (2015). The efficacy of neurofeedback for the treatment of autism spectrum disorders: A systematic review. Revista de Psicopatologia y Piscolgia Clinica, 20(2), 151-163.

Haddi, P. Haghshenas, S., & Rostami, R. (2011). Rehabilitation in autism spectrum disorder (ASD): A mixture of neurofeedback training and auditory integration training (AIT). Procedia – Social and Behavioural Sciences, 30, 611-614.

Jarusiewicz, B. (2002). Efficacy of neurofeedback for children in the autistic spectrum: A pilot study. Journal of Neurotherapy, 6(4), 39-49.

Karimi, M., Haghshenas, S., & Rostami, R. (2011). Neurofeedback and autism spectrum: A case study. Procedia – Social and Behavioural Sciences, 30, 1472-1475.  

Kouijer, M., de Moor, J., Gerrits, B., Congedo, M., & van Schie, H. (2009). Neurofeedback improves executive functioning in children with autism spectrum disorders. Research in Autism Spectrum Disorders, 3(1), 145-162.

Kouijer, M., Van Schi, H., Gerrits, B., Buitelaar, J., & De Moor, J. (2013). Is EEG-biofeedback an effective treatment in autism spectrum disorders? A randomised controlled trial. Applied Psychophysiology Biofeedback, 38(1), 17-28.

Linden M, Habib T, Radojevic V (1996) A controlled study of the effects of EEG biofeedback on cognition and behavior of children with attention deficit disorder and learning disabilities. Biofeedback Self Regul 21:35–49

Linden M (2004) Case studies of QEEG mapping and neurofeedback with autism. Presented at the 12th Annual Conference of the International Society for Neuronal Regulation, Fort Lauderdale, Florida

Mathewson, K. J., Jetha, M. K., Drmic, I. E., Bryson, S. E., Goldberg, J. O., and Schmidt, L. A. (2012). Regional EEG alpha power, coherence, and behavioral symptomatology in autism spectrum disorder. Clin. Neurophysiol. 123, 1798–1809. doi:10.1016/j.clinph.2012.02.061

Putman, P., Verkuil, B., Arias-Garcia, E., Pantazi, I., & van Schie, C. (2014). EEG theta/beta ratio as a potential biomarker for attentional control and resilience against deleterious effects of stress on attention. Cognitive, Affective, & Behavioral Neuroscience, 14(2), 782-791.

Pineda, J., Carrasco, K., Datko, M., Pillen, S., & Schalles, M. (2014). Neurofeedback training produces normalisation in behavioural and electrophysiological measures of high-functioning autism. Philosophical transactions of the Royal Society of London, Series B, Biological Sciences, 369(1644), 20130183.

Singer, W., & Gray, C. M. (1995). Visual feature integration and the temporal correlation hypothesis. Annual review of neuroscience, 18(1), 555-586.

Tansey MA (1993) Ten year stability of EEG biofeedback results for a 10 year old hyperactive boy who failed fourth grade in a class for the perceptually impaired. Biofeedback Self Regul 18:33–44

Zivoder, I., Martic-Biocina, S., Kosic, A., & Bosak, J. (2015). Neurofeedback application in the treatment of autistic spectrum disorders (ASD). Psychiatria Danubina, 27, 391-394.