Scientific evidence on Brain Map (qEEG)

There are thousands of research studies on qEEG for a wide variety of clinical conditions, including memory problems, anxiety, depression, traumatic brain injury (TBI), attention deficit (ADD / ADHD) and processing problems in spectrum disorder. autistic (ASD). Look at the most important studies:

Attention Deficit ADD / ADHD

Attention deficit has been associated with deviant activity in the frontal brain areas. Most research has shown that ADHD patients show excess frontal theta (eg, Arns et a high frontal theta / beta ratio (eg, Snyder et al., 2015). It has also been associated with a number of other deviations, such as an excess of frontal beta (beta spindling; Clarke et al., 2001) and a low frequency of alpha spike (Arns et al., 2008). A large number of investigations have shown that neurofeedback is an effective treatment for ADHD (Arns et al., 2009) .QEEG-guided neurofeedback protocols have been shown to be highly effective, with a 76% response rate and an effect size of 1.78 (Arns et al. al., 2012).


Anxiety disorder has been associated with increased beta activity (Isotani et al., 2001; Pavlenko et al., 2009), decreased alpha activity in occipital brain regions (Pavlenko et al., 2009), and an increase in alpha activity in the right fronto-lateral brain regions (Davidson et al., 2000). Research on the efficacy of neurofeedback for treating anxiety suggests that increased alpha activity may have anxiolytic effects (Hardt & Kamiya, 1978; Wang et al., 2014. However, large well-controlled studies are currently lacking. Cheon et al. (2015) suggest that effective neurofeedback protocols must be guided by the patient’s qEEG.


Depression has been associated with increased alpha asymmetry in the frontal lobes (Thibodeau et al., 2006). In other words, depression is correlated with an increase in alpha potency in the left frontal lobe and a decrease in the right frontal lobe. Neurofeedback protocols that have been shown to be effective in treating depression are alpha asymmetry training (Baehr et al., 1997; Choi et al., 2011) and lowering theta and increasing beta (Walker and Lawson, 2013). Another effective approach to treating depression is the “Peniston protocol” which is a combination of biofeedback, neurofeedback, and psychotherapy (eg, Saxby & Penniston, 1995). The neurofeedback element of the Peniston protocol focuses on increasing occipital alpha and theta potency.


Schizophrenia has been associated with increased frontal delta and theta power, decreased alpha power, and increased beta power (see Boutros et al., 2008, for an overview). There is also growing evidence that schizophrenia is related to altered gamma activity (eg, Lee et al., 2003). Auditory verbal hallucinations (AVHs) occur in approximately 3 out of 4 patients with schizophrenia (McCarthy-Jones, 2012). Gamma potency training results in improved processes related to trait binding and contextual memory (Keizer et al., 2010a; 2010b). These processes are believed to play a causal role in AVHs and therefore it has been hypothesized that gamma activity training could be effective in reducing AHVs (McCarthy-Jones, 2012).

Surmeli et al. (2012) showed that qEEG-guided neurofeedback protocols were effective in treating schizophrenia. However, future studies implementing large randomized controlled trials are needed to determine whether neurofeedback is effective in treating schizophrenia. Therefore, patient-specific qEEG-guided protocols are recommended.

Memory Disorders

Memory disorders, such as Alzheimer’s disease, have been associated with increased delta and theta potency and decreased alpha and beta potency (for an overview see: Dauwel et al., 2010). There have been very few studies exploring the possibility of treating memory disorders using neurofeedback. However, Neurofeedback studies with healthy subjects have shown that individual high alpha training (Egner et al., 2005; Zoefel et al., 2011; Guez et al., 2015), Sensorimotor Rhythm (SMR; Egner & Gruzelier, 2001; Guez et al., 2015) and gamma potency (Keizer et al., 2010a; 2010b) have beneficial effects on long-term memory performance.


There is strong evidence that insomnia is associated with increased beta and gamma potency, presumably caused by hyper-alertness (for an overview see: Perlis et al., 2001; Bonnet et al., 2010). Research on neurofeedback has shown that Sensorimotor Rhythm (RMS) training and the regulation of Slow Cortical Potentials (PCL) are effective in treating insomnia (Arns et al., 2014). The RMS boost results in a decrease in sleep latency (Hoedlmoser et al., 2008) and an increase in total sleep time (Cortoos et al., 2010; Hoedlmoser et al., 2008). SMR training also leads to increased sleep spindle density during sleep spindle density (Hoedlmoser et al., 2008; Sterman et al., 1970), presumably the result of spectral overlap between RMS and sleep spindle activity.

Obsessive Compulsive Disorder (OCD)

OCD has been associated with excess theta and alpha (Prichep et al., Surmeli et al., 2011) and excess slow alpha activity (Bolwig et al., 2007). There are two case studies that used alpha enhancement neurofeedback for treating OCD (e.g. Mills and Solyum, 1974; Glucek and Stroebel, 1975). Even though some patients seemed to benefit from alpha enhancement neurofeedback, the results were mixed. The diversity of the QEEG deviations is the reason why Surmeli et al. (2011) adopted an approach with which Neurofeedback protocols were guided by the QEEG deviations of the patient. The most commonly used protocols were based on downregulating theta or alpha on frontal electrode sites. Similarly, Koprivova et al. (2013) used QEEG−guided protocols in a double−blind, placebo controlled trial and demonstrated that neurofeedback was superior to sham neurofeedback in treating OCD. The lack of large randomized controlled trials justifies the use of QEEG and favors an important role for comorbid disorders in determining the neurofeedback protocol.


Autism has been associated with a dysfunctional mirror neuron system. Mirror neuron activity can be observed in the EEG as a decrease of alpha power over the sensori−motor strip (so−called Mu suppression) as a result of observing human motor actions (as opposed to one’s own motor actions). Patients suffering from autism often fail to show Mu suppression (Oberman et al., 2005). Downtraining Mu activity has shown to result in a significant decrease of autism−related symptoms (Pineda et al., 2008). Kouijzer et al. (2009a) showed that downtraining theta activity and simultaneously uptraining beta activity has positive effects on both executive functions and social behavior in autistic children. These effects persisted 12 months after treatment (Kouijzer et al., 2009b). Finally, it has been shown that QEEG based neurofeedback protocols are effective in treating autism (Coben and Padolsky, 2007; Jarusiewicz, 2002).  


Research on the neural correlates of addiction has focused primarily on the effects of specific substances on the EEG in the resting state. resting state EEG. Depending on the substance to which the patient is addicted, the abnormalities in the resting-state EEG can be quite diverse. resting-state EEG can be quite diverse. The “Peniston protocol” uses a combination of biofeedback, neurofeedback, and psychotherapy and has been shown to be effective in treating addiction (see: Sokhadze et al., 2008 for an overview). The Peniston protocol consists of increasing the alpha and theta alpha and theta power to induce a hypnagogic state. The efficacy of combining the Peniston protocol with theta/beta training has also been demonstrated (Scott et al., 2005). This approach has been termed the Scott-Kaiser modification (of the Peniston protocol).


Epilepsy can be the result of many different causes, such as genetics, head trauma, infections and brain tumors. EEG recordings during an epileptic seizure are often characterized by trains of high−amplitude spike−wave complexes that occur in the delta frequency range. The EEG during periods where the patient is not experiencing an epileptic seizure often also shows abnormalities, such as spikes, sharp waves and spike−wave complexes. The emergence of the field of neurofeedback for clinical applications started with the seminal work of Wyrwicka and Sterman (1968), which showed that uptraining Sensori−Motor Rhythm (SMR) increases the seizure threshold in epileptic cats. A recent meta−study showed that uptraining SMR is effective in treating epilepsy in humans (Tan et al., 2009).

Traumatic Brain Injury (TBI)

TBI is a heterogeneous disorder, since differences in the cause, location and degree of tissue damage lead to differences in both the EEG deviances and functional impairments. For that reason, most studies on the effect of neurofeedback treatment for TBI patients have relied on QEEG−guided protocols e.g. Tinius & Tinius, 2000; (Hoffman et al., 1996). However, there are currently no randomized controlled trials that show a superior effect of neurofeedback over placebo in the treatment of TBI. It is recommended to use QEEG−guided protocols which are co−determined by the comorbidities of the patient.


Tinnitus corresponds with decreased alpha activity and increased delta, theta and gamma activity in the auditory regions of the brain in the absence of auditory stimulation (Llinas et al., 2005; Van Der Loo et al., 2009; Weisz et al., 2005). Alpha activity has been associated with inhibitory processes, which is the reason that a number of studies have focused on enhancing temporal alpha power using neurofeedback (e.g. Crocetti et al., 2011; Dohrmann et al., 2007a, b; Hartmann et al., 2014). The results of these studies show that neurofeedback can be effective in treating tinnitus.


Dyslexiahas mainly been related with deviant activity in left temporal brain areas (e.g. Klimesch et al., 2001). However, it has been shown that dyslexia can be associated with deviant activity in a variety of brain regions other than left temporal areas (e.g. Ackerman & Dykman, 1995; Arns et al., 2007;Flynn et al., 1992). Moreover, dyslexia has been associated with excess beta (e.g. Klimesch et al., 2001), but also with excess delta and theta (Arns et al., 2007). Arns et al. (2010) showed that qEEG guided neurofeedback protocols can be effective in treating dyslexia.