top of page

A Deep Dive into the Alpha Rhythm

Updated: Jun 25


brainwaves

Does the Alpha Rhythm Range From 8-12 or 8-13 Hz?

In electroencephalography (EEG), the alpha rhythm is generally defined as having a frequency range of 8-12 Hz or 8-13 Hz. This discrepancy arises from variations in historical definitions, regional practices, and updates in scientific standards.


Many sources traditionally define the alpha rhythm within the 8-12 Hz range. This is common in older literature and is used in some clinical contexts to describe the typical frequency band associated with a relaxed, awake state. For example, Jadeja (2021) describes clinically relevant EEG frequency bands, including the alpha rhythm, as 8-12 Hz.


More recent or alternative standards may extend this range to 8-13 Hz to accommodate a broader spectrum of alpha activity observed in various populations and conditions. For example,

Kane and colleagues (2017) offered the following revised definition:


Rhythm at 8–13 Hz inclusive occurring during wakefulness over the posterior regions of the head, generally with maximum amplitudes over the occipital areas. Amplitude varies but is mostly below 50 µV in the adult, but often much higher in children. Best seen with the eyes closed, during physical relaxation and relative mental inactivity. Blocked or attenuated by attention, especially visual, and mental effort. Comment: use of term rhythm must be restricted to those rhythms that fulfill these criteria. Activities in the alpha band which differ from the alpha rhythm as regards their topography and/or reactivity, should either have specific appellations (for instance: the mu rhythm and alpha coma) or should be referred to as rhythms of alpha frequency or alpha activity.

Such a rhythm must meet all the above criteria to qualify for the designation, eliminating patterns such as mu rhythm in the Rolandic areas, which often have the same frequency but different morphology (characteristic wave shape and pattern) and behavioral correlations.

Summary

The slight variation in definitions typically does not impact the practical applications significantly but reflects different methodological approaches or updates in EEG research standards. The choice of definition can depend on the specific requirements of a study or the preference of a clinical guideline being followed. Both ranges are correct.


The Source of the Alpha Rhythm


Activity in the 8-12 Hz frequency range appears associated with reduced sensory and cognitive activity. Why is this so? What mechanism is responsible for this rhythmic activity?


One of the main communication pathways between the external world, the senses that perceive and transmit this information, and the cortical neurons that receive it is the thalamic-cortical relay system, often designated the TCR system


The thalamus receives incoming sensory input (a paired structure in the brain's center – see the image below). The individual nuclei of the thalamus transmit that sensory information to appropriate areas of the cortex. The occipital and parietal areas of the cortex are the primary visual processing areas, just as the temporal areas process most of the auditory information. In contrast, central Rolandic areas process tactile and other signals from the skin and muscles. Of course, current findings indicate that brain activity is associated with coordination within and between cortical networks and influences from subcortical structures and local and TCR influences. Still, the TCR system is a primary pathway for determining which cortex areas receive each type of sensory input. Thalamic-cortical relay system graphic © Elsevier Inc. - Netterimages.com.


Thalamus





frequency

Caption: An eyes-closed EEG in a longitudinal bipolar montage is represented as a spectral display. The x-axis shows frequency from 0-30 Hz, and the y-axis shows absolute power (uV Sq). Note the peak at about 10 Hz. Voltage is higher in the P3-O1 derivation than in the P4-O2 derivation, revealing a small asymmetry. The highest power is in the T4-T6 derivation.


Identifying alpha activity in the scalp's parietal and/or occipital areas is usually quite easy, particularly in the eyes-closed condition. The image below shows an eyes-closed alpha pattern from a 15 -year-old male.

alpha

Caption: eyes-closed EEG filtered to 1-45 Hz in the longitudinal bipolar montage with a 50-uV scale. Boxes indicate the most prominent 8-12 Hz activity. This montage represents a series of adjacent electrode comparisons or derivations, as each signal tracing is derived from each pair of electrode comparisons.


A longitudinal bipolar montage is displayed below.


longitudinal montage

Caption: Longitudinal bipolar montage (commonly known as the “double banana” montage).


Observe that the rhythmic activity is well-defined and has the typical bursting or spindling pattern of the alpha rhythm, resulting from the input of the TCR and NRT systems. Spindling consists of a series of distinct oscillations of a particular frequency that begin with relatively low amplitude, increase in amplitude, and then decrease in amplitude, giving the appearance of a spindle such as one used in spinning, with fiber wound around it. Graphic © New Africa/Shutterstock.com.


spindle

The waves are quite sinusoidal (waving up and down in a smooth rhythm similar to a sine curve) and continue throughout the recording with minimal disruption. The voltage indicator shows that the maximum voltage at the moment of the line placement was from about 20 to 30 uV at the peak of the waveform in this montage.


One can determine the wave's frequency by counting the number of peaks in a one-second segment or counting the number of times the wave crossed the zero line and dividing by 2 (zero crossings/2). Either method gives an 8-9 Hz value when multiple one-second epochs are counted. This is somewhat slow for a 15-year-old, although the voltage appears to be within normal limits. When compared to a normative database, we can see that, indeed, it is a slow peak alpha when compared to other 15-year-old males, as indicated by the chart below from the NeuroGuide database.


NeuroGuide

Caption: the alpha peak frequency z-scores are at least 1 SD below expected values (> -1.0 SD) in all locations, and many areas show deviations exceeding the significance cutoff of -1.96 SD (blue highlight). The database also plots the theta peak frequency as fast (red highlight) due to slow alpha in the 8-Hz frequency bin or segment. There are likely other slow components of the dominant rhythm that contribute to this incorrect plot of the frequency information. Ideally, the peak frequency of the EEG should be calculated within a broad range from approximately 6 Hz to about 14-16 Hz to avoid this type of error. This is another problem with the somewhat arbitrary designation of frequency bands using set values.


The same data processed by the iSynchBrain database show similar findings for O1 and O2 below.

spectral

Caption: Peak frequency power and frequency comparisons at O1 and O2 show a slow peak frequency at 9.2 Hz bilaterally, resulting in z-scores of -1.15 and -1.23, respectively. Voltage (power) on the left is 0.63 SD, and on the right is 1.01 SD, showing slightly elevated values. This database provides single-Hz calculations for frequency and amplitude rather than using an arbitrary band to define alpha.



The alpha peak frequency is age-dependent, though not called "alpha" until the frequency reaches 8 Hz. It is designated as the posterior basic rhythm or posterior dominant rhythm (PDR) in early infancy. It appears around 4 months with a frequency of about 4 cycles per second (c/s) or Hz (Schomer & Lopes da Silva, 2017). The PDR increases (speeds up) during maturation and is approximately 6 c/s at 1 year and up to 8 c/s at around 3 years of age. This is when it can be called the alpha rhythm.


The frequency reaches 10 Hz by approximately 10 years of age, and that (10.2 ± 0.9/sec) is the peak frequency of adulthood (Petersén & Eeg-Olofsson, 1971). The previous example shows why a peak frequency between 8-9 Hz is slow for a 15-year-old.



The Alpha Frequency's Meaning and Importance


The speed or frequency of the alpha peak frequency is often mentioned, even in a neurologist’s report. For the neurofeedback practitioner, it is helpful to understand the factors associated with different alpha frequencies. The alpha peak frequency measures the frequency of the rhythmic pattern of the posterior rhythm (generally called alpha). This has traditionally been an important measure. Although it has recently been somewhat de-emphasized in some EEG circles, it remains an interesting measure. A great deal of research supports it as a useful metric for assessment. Researchers such as Klimesch and others (Haegens et al., 2014; Hanslmayr et al., 2005; Mierau et al., 2017) have found it important to first assess the individual peak alpha frequency for normal subjects before investigating the cognitive effects of alpha neurofeedback.


A slow peak alpha frequency has been associated with some forms of cognitive decline and memory impairment (López-Sanz et al., 2016), as well as mild traumatic brain injury (mTBI; Jabbari et al., 1985; Williams, 1941). A fast peak alpha frequency has been associated with improved scores on timed IQ tests. It has also been associated with enhanced memory and cognitive performance in various age groups (Grandy et al., 2013). A faster peak alpha frequency is associated with advanced reading skills in precocious children (Suldo et al., 2001).

Negative correlations of a peak alpha frequency faster than 10.5 Hz, possibly associated with an overly activated central nervous system, may include sleep initiation problems, anxiety, intrusive thoughts, and difficulty with self-soothing and self-calming skills.

The peak alpha frequency changes throughout the lifespan. Therefore, age-normed values for the peak alpha frequency are important for assessment purposes. The normal adult peak alpha frequency is 9.5-10.5 Hz (Jabbari et al., 1985). Scholarly literature states that the peak alpha frequency is not abnormal until it is below 8 Hz (Schomer & Lopes da Silva, 2017). However, it is commonly thought to be potentially meaningful when the frequency is below 9 Hz for an adult.


Angelakis et al. (2004) stated that slowing the alpha peak frequency by more than 1 Hz (9 Hz for an adult) is generally a sign of pathology. A slow alpha frequency can be associated with fatigue, cognitive decline, and memory impairment. Slowing of the background alpha rhythm is also a sign of generalized cerebral dysfunction (Nayak & Anilkumar, 2021). Rathee and colleagues (2020) related the speed of the peak alpha frequency to reading comprehension. They found that a slower peak alpha frequency is associated with poor comprehension.


Asymmetries in the posterior rhythm are common, and some are expected. Typically, the posterior rhythm has a higher voltage over the right, nondominant hemisphere. Surprisingly, a complete absence of the posterior rhythm can occur in a small percentage of otherwise normal individuals. While the absence of the posterior rhythm can also be seen in individuals with brain injuries or other abnormalities, such cases usually exhibit additional EEG abnormalities. Therefore, an EEG that only shows the absence of the posterior rhythm without other abnormalities should be considered normal (Libenson, 2024).


However, other sources correlate the absence of the posterior alpha rhythm with a variety of other indicators, even when additional abnormal EEG patterns are not present.


Niedermeyer (1997) discusses the absence of the posterior alpha rhythm and points out correlations between chronic alcoholism and vertebrobasilar artery insufficiency. He suggests that a lack of posterior alpha may be associated with dysfunctional synchronization mechanisms and states:


“The crucial factor in these considerations might be the question: is absence of the alpha rhythms in the scalp EEG (and even in recordings from deeper structures) synonymous with alpha absence in the microstructure? In other words: is alpha rhythm a truly universal phenomenon in healthy persons regardless of EEG-alpha-absence caused by lack of synchronizing mechanisms? Accordingly, persons with an inherited low voltage fast pattern do have a posterior alpha but are unable to show it in their EEG records.”


Other conditions that show an absence of the posterior alpha rhythm include seizure disorders. Aich (2014) and colleagues found a significant correlation (<0.01) between the presence of seizure activity and the absence of the alpha rhythm. This study identified 48.3% of their identified seizure disorder participants as having no visible alpha rhythm.



Eyes-Closed Alpha Response Voltage Meaning and Importance


The amplitude of the activity can also be meaningful in addition to the peak frequency within the 8-12 or 8-13 Hz alpha band. Anxiety and TBI can reduce alpha activity during baselines. Since concentration and thinking can suppress alpha, instruct clients to refrain from these activities during recording (Demos, 2019).


The alpha voltage will be partially affected by the montage that is used. For example, considering the discussion of differential amplifiers in the Instrumentation and Electronics section, the closer two electrodes are to each other, the more the rhythmic, synchronous patterns will be attenuated. Common-mode rejection (CMR) is most sensitive to frequency synchronization, so waves that are the same frequency and also synchronous (e.g., 10 Hz waves, waving up and down at the same time at the two sensor locations + and – [also commonly called “active” and “ reference”]) will be rejected. Comparing the O1 and O2 occipital electrodes to each other will result in a lower apparent alpha voltage if the two waveforms are synchronous, which is quite likely.


Conversely, comparing either O1 or O2 to an ear reference or possibly to a forehead reference would result in almost no rejection of alpha activity. Therefore, the rhythmic patterns are unlikely to be similar at these distant locations and will be retained. When viewing standard voltage information in an atlas, a research paper, or a textbook, try to identify the montage used when those standards were developed.


Simonova et al. (1967) found amplitudes between 20 and 60 μV in 66% of their subjects, while values below 20 μV were found in 28% and above 60 μV in only 6%. Schomer and Lopes da Silva (2017) suggest that values between 10 and 60 μV are typical.  However, other sources such as the John Hopkins Atlas of EEG (2011) and Libenson’s (2024) Practical Approach to Electroencephalography (2nd ed.) cite 20 μV as the minimum voltage for adults. These differences may seem insignificant but can represent the difference between a low voltage fast EEG finding and a typical assessment. 


Rhythmic alpha activity represents the synchronization of the EEG. It represents part of the excitation/inhibition cycle. When either large or small groups of neurons perform tasks, this results in the desynchronization of the EEG during work, as each group of neurons performs its function locally and independently. This is followed by a resting or inhibitory phase that results in the synchronization of the EEG and, hence, an increase in alpha amplitude as many neurons fire synchronously. This is seen in the shift from active visual processing when the eyes are open to a synchronous pattern of oscillatory activity when neurons do not have incoming visual input to process and can rest. This measure of alpha voltage change from eyes open to eyes closed, known as the alpha response, and the decrease of alpha with eyes opening, called alpha blocking, helps identify if the work/rest cycle is occurring correctly.


Someone with an eyes-closed posterior dominant rhythm voltage below 20 μV suggests to the neurofeedback assessor that the person does not easily shift to a state of decreased arousal/alertness necessary for the alpha amplitude to increase. The disconnection from the outside world upon eyes closing should result in decreased sensory processing of vision and other senses and decreased cognitive activity, leading to an increase in 8-12 Hz amplitude or power. 


Typically, alpha activity voltage should increase as more neurons fire synchronously in this frequency. When this increase is less than 50% above the resting baseline eyes-open alpha voltage, it usually indicates some difficulty turning off the mind, meaning that neurons remain activated and working and thus prevent them from entering a resting state. The lack of a typical alpha increase may be associated with heightened states of alertness and vigilance, meaning that these clients maintain their external perceptive focus and/or cognitive activity, even when the eyes are closed, likely inhibiting the ability to achieve global synchronous activity. Resulting behavioral consequences can include fatigue, as the neurons are constantly engaged and are not allowed to rest. This pattern may be associated with a history of trauma and/or a history of hypervigilance for various reasons.


Example of a typical alpha response upon eyes closing.

eyes-closed

Caption: This is a 19-channel recording of the transition from eye-open to eye-closed conditions. It is a longitudinal bipolar montage, and the scale is 50 μV. Note that the eyes close at minute 2:40 (indicated by the eye movement), and the immediate response of the alpha rhythm appears.



The movie below is a 19-channel BioTrace+ /NeXus-32 display of alpha activity under eyes-closed and eyes-open conditions © John S. Anderson.





Alpha-Blocking Response Meaning and Importance


Conversely, the continued presence of alpha once the eyes are open suggests a lack of appropriate alpha blocking. This appears to result from a lack of inhibition of synchronous generator mechanisms. Hartoyo and colleagues (2020) showed a simple mechanism: excitatory input to inhibitory cortical neurons. This differs from the excitatory cortical neurons that typically reduce synchronous cortical firing in favor of local responses to incoming stimuli when the eyes are opened.


Note that activation of inhibitory mechanisms results in increased inhibition, even though the function is initially excitatory. Conversely, activation of excitatory mechanisms results in greater activation. This can seem confusing, and it may help to focus on the result, whether excitatory or inhibitory, rather than the initial behavior.


There should be a dynamic balance between excitation and inhibition in the human neocortex (Dehghani et al., 2016). When this balance is disrupted, we see the behavioral effects noted here. Alpha blocking represents the re-activation of visual processing neurons when visual input returns. Typically, as these neurons are no longer in a common or general resting state but are involved in task-oriented behaviors that are more localized, synchronous activity should decrease (be inhibited). Therefore, the overall voltage will decrease because of less synchronization. This does not imply that more neurons are firing when the alpha amplitude is higher; it means less synchronization and, hence, lower voltage.


Imagine an auditorium where everyone claps in unison (eyes-closed alpha rhythm). The noise is loud because the claps are synchronized (higher amplitude), with silence in between. Now, picture everyone clapping independently, perhaps in sync with immediate neighbors but not the whole audience. This resembles the eyes-open condition when recording the alpha rhythm: there is continuous noise because someone is always clapping, resulting in a faster frequency of claps. However, it’s never as loud as synchronized clapping, leading to lower overall voltage despite the higher frequency. Thus, alpha amplitude decreases quickly when eyes open, within 1-2 seconds, and certainly within 10-15 seconds. Delays in alpha blocking suggest difficulty returning to the task. Desynchronization occurs in posterior areas as visual processing begins when the eyes are opened.


The most common reasons for the lack of appropriate alpha blocking (meaning alpha activity persists after eyes are opened) are:


1. Fatigue, including sleep deprivation


2. Long-term meditation practice, particularly mantra meditation


3. Marijuana use and abuse, generally long-term, chronic


4. Cerebral dysfunction due to disease, injury, or possibly chemical exposure



Below is an example of correct alpha blocking following the eyes opening.

longitudinal

Caption: This is a 19-channel recording of the transition from eyes-closed to eyes-open conditions. This is a longitudinal bipolar montage, and the scale is 50 μV. Note the eyes opening at minute 1:12 (indicated by the eye movement) and the immediate blocking of the alpha rhythm.



Clearly, the response of 8-12 Hz EEG activity can be quite revealing and provides the clinician with helpful information about the client. However, it is important to note that other factors can affect the EEG recording. We have already noted the effects of artifacts on the EEG in general. Additionally, the client’s state of mind, level of anxiety, comfort with the application of sensors to the scalp, level of trust of the practitioner conducting the recording, amount of sleep, the use of caffeine and other stimulants and common medications can all affect the results of the recording.


Once an assessment is made of excess or deficient alpha activity, lack of an alpha response or persistent alpha following the eyes opening, or a slow or fast peak alpha frequency, the clinician can proceed with training to address these findings. There are multiple approaches to training the 8-12 Hz frequency band, including training specific segments of that band to achieve training goals. For example, if a lack of an alpha response to eyes closing is associated with anxiety and possibly insomnia, training for an increase in the 8-10 Hz portion of the posterior alpha rhythm in the eyes-closed condition may be an effective intervention. If the peak alpha frequency is slow, training for increases in the 10-12 Hz portion may help speed up this frequency. If there is persistent alpha in the eyes-open condition, inhibiting or downtraining 8-12 Hz generally may be helpful.


Of course, with any intervention, other causal factors must be addressed as well. Persistent alpha and/or frontal alpha can signify fatigue secondary to a sleep disorder such as sleep apnea. Therefore, a referral to a physician for a sleep study may be helpful. Over-arousal patterns that correspond to a lack of alpha response can be associated with a history of emotional, psychological, physical, or sexual trauma. The neurofeedback clinician may need to address these issues or refer the client to an appropriate therapist.



Glossary


alpha blocking: alpha blocking normally occurs when eyes have just been opened. Arousal and specific forms of cognitive activity may reduce alpha amplitude or eliminate it while increasing EEG power in the beta range.


alpha response: increased alpha amplitude.


alpha rhythm: an 8-12-Hz activity that depends on the interaction between rhythmic burst firing by a subset of thalamocortical (TC) neurons linked by gap junctions and rhythmic inhibition by widely distributed reticular nucleus neurons. Researchers have correlated the alpha rhythm with relaxed wakefulness. Alpha is the dominant rhythm in adults and is located posteriorly. The alpha rhythm may be divided into alpha 1 (8-10 Hz) and alpha 2 (10-12 Hz).


alpha spindles: trains of alpha waves visible in the raw EEG and observed during drowsiness, fatigue, and meditative practice.


reticular nucleus of the thalamus (TRN): GABAergic thalamic neurons that modulate signals from other thalamic nuclei and do not project to the cortex. Also called the nucleus reticularis of the thalamus (NRT).


thalamic-cortical relay (TCR) system: the primary pathway for determining which cortical areas receive each type of sensory input.



References

Aich T. K. (2014). Absent posterior alpha rhythm: An indirect indicator of seizure disorder? Indian Journal of Psychiatry, 56(1), 61–66. https://doi.org/10.4103/0019-5545.124715

Angelakis, E., Lubar, J. F., Stathopoulou, S., & Kounios, J. (2004). Peak alpha frequency: an electroencephalographic measure of cognitive preparedness. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 115(4), 887–897. https://doi.org/10.1016/j.clinph.2003.11.034 Crabtree J. W. (2018). Functional diversity of thalamic reticular subnetworks. Frontiers in Systems Neuroscience, 12, 41. https://doi.org/10.3389/fnsys.2018.00041


Dehghani, N., Peyrache, A., Telenczuk, B., Le Van Quyen, M., Halgren, E., Cash, S. S., Hatsopoulos, N. G., & Destexhe, A. (2016). Dynamic balance of excitation and inhibition in human and monkey neocortex. Scientific Reports, 6, 23176. https://doi.org/10.1038/srep23176

Demos, J. N. (2019). Getting started with neurofeedback (2nd ed.). W. W. Norton & Company.

Grandy, T. H., Werkle-Bergner, M., Chicherio, C., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2013). Peak individual alpha frequency qualifies as a stable neurophysiological trait marker in healthy younger and older adults. Psychophysiology, 50(6), 570–582. https://doi.org/10.1111/psyp.12043  

Haegens, S., Cousijnc, H., Wallis, G., Harrison, P. J., & Nobre, A. C. (2014). Inter- and intra-individual variability in alpha peak frequency.  NeuroImage, 92, 46-55. doi.org/10.1016/j.neuroimage.2014.01.049 Hanslmayr, S., Sauseng, P., Doppelmayr, M., Schabus, M., & Klimesch, W. (2005).  Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects. Applied Psychophysiology and Biofeedback 30, 1–10. https://doi.org/10.1007/s10484-005-2169-8

Hartoyo, A., Cadusch, P. J., Liley, D. T. J., & Hicks, D. G. (2020). Inferring a simple mechanism for alpha-blocking by fitting a neural population model to EEG spectra. PLoS Computational Biology, 16(4), e1007662. https://doi.org/10.1371/journal.pcbi.1007662

Jadeja, N. M. (2021). Frequencies and rhythms. In How to read an EEG. Cambridge University Press. https://doi.org/10.1017/9781108918923.008 Kane, N., Acharya, J., Benickzy, S., Caboclo, L., Finnigan, S., Kaplan, P. W., Shibasaki, H., Pressler, R., & van Putten, M. J. A. M. (2017). A revised glossary of terms most commonly used by clinical electroencephalographers and updated proposal for the report format of the EEG findings. Revision 2017. Clinical Neurophysiology Practice, 2, 170–185. https://doi.org/10.1016/j.cnp.2017.07.002 Libenson, M. H. (2024). Practical approach to electroencephalography. Elsevier.

López-Sanz, D., Bruña, R., Garcés, P., Cámara, C., Serrano, N., Rodríguez-Rojo, I., Delgado, M., Montenegro, M., López-Higes, R., Yus, M., & Maestú, F. (2016). Alpha band disruption in the AD-continuum starts in the Subjective Cognitive Decline stage: A MEG study. Scientific Reports, 6. https://doi.org/10.1038/srep37685.

Mierau, M., Klimesch, W., & Lefebvre, J. (2017). State-dependent alpha peak frequency shifts: Experimental evidence, potential mechanisms and functional implications. Neuroscience, 360, 146-154. doi.org/10.1016/j.neuroscience.2017.07.037


Min, B. K., & Park, H. J. (2010). Task-related modulation of anterior theta and posterior alpha EEG reflects top-down preparation. BMC Neuroscience, 11, 79. https://doi.org/10.1186/1471-2202-11-79

Neurophysiological parameters influencing sleep–wake discrepancy in insomnia disorder: A preliminary analysis on alpha rhythm during sleep onset. (2023). Brain Sciences. MDPI. https://www.mdpi.com/2076-3425/13/2/260 Niedermeyer E. (1997). Alpha rhythms as physiological and abnormal phenomena. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 26(1-3), 31–49. https://doi.org/10.1016/s0167-8760(97)00754-x

Nunez, P. L., & Srinivasan, R. (2019). Electric fields of the brain: The neurophysics of EEG (2nd ed.). Oxford Academic. https://doi.org/10.1093/acprof:oso/9780195050387.001.0001


Petersén, I., & Eeg-Olofsson, O. (1971). The development of the electroencephalogram in normal children from the age of 1 through 15 years. Non-paroxysmal activity. Neuropadiatrie, 2(3), 247–304. https://doi.org/10.1055/s-0028-1091786


Rathee, S., Bhatia, D., Punia, V., & Singh, R. (2020). Peak alpha frequency in relation to cognitive performance. Journal of Neurosciences in Rural Practice, 11(3), 416–419. https://doi.org/10.1055/s-0040-1712585


Schomer, D. L., & Lopes da Silva, F. H. (Eds.). (2017). Niedermeyer's electroencephalography: Basic principles, clinical applications, and related fields (7th ed.). Oxford Academic. https://doi.org/10.1093/med/9780190228484.001.0001


Simonová, O., Roth, B., & Stein, J. (1967). EEG studies of healthy population--Normal rhythms of resting recording. Acta Universitatis Carolinae. Medica, 13(7), 543–551. PMID: 5620888


Suldo, S. M., Olson, L. A., & Evans, J. R. (2002). Quantitative EEG Evidence of Increased Alpha Peak Frequency in Children with Precocious Reading Ability. Journal of Neurotherapy, 5(3), 39–50. https://doi.org/10.1300/J184v05n03_05

The Johns Hopkins atlas of digital EEG: An interactive training guide. (2011). Johns Hopkins University Press.

Williams D. (1941). The electro-encephalogram in acute injuries. Journal of Neurology and Psychiatry, 4(2), 107–130. https://doi.org/10.1136/jnnp.4.2.107



Learn More


We have launched our $150 qEEG Tutor for Exam Review to help you prepare for the International QEEG Certification Board exam.

qEEG Tutor


Support Our Friends at ISNR and AAPB


ISNR


AAPB

Comments

Rated 0 out of 5 stars.
No ratings yet

Add a rating
bottom of page