This study demonstrates that the EEG phenotypes as described by Johnstone, Gunkelman & Lunt are identifiable EEG patterns with good inter-rater reliability. Furthermore, it was also demonstrated that these EEG phenotypes occurred in both ADHD subjects as well as healthy control subjects. The Frontal Slow and Slowed Alpha Peak Frequency and the Low Voltage EEG phenotype discriminated ADHD subjects best from controls (however the difference was not significant). The Frontal Slow group responded to a stimulant with a clinically relevant decreased number of false negative errors on the CPT. The Frontal Slow and Slowed Alpha Peak Frequency phenotypes have different etiologies as evidenced by the treatment response to stimulants. In previous research Slowed Alpha Peak Frequency has most likely erroneously shown up as a frontal theta sub-group. This implies that future research employing EEG measures in ADHD should avoid using traditional frequency bands, but dissociate Slowed Alpha Peak Frequency from frontal theta by taking the individual alpha peak frequency into account. Furthermore, the divergence from normal of the frequency bands pertaining to the various phenotypes is greater in the clinical group than in the controls. Investigating EEG phenotypes provides a promising new way to approach EEG data, explaining much of the variance in EEGs and thereby potentially leading to more specific prospective treatment outcomes.

We propose development of evidence-based methods to guide clinical intervention in neurobehavioral syndromes based on categorization of individuals using both behavioral measures and quantification of the EEG (qEEG). Review of a large number of clinical EEG and qEEG studies suggests that it is plausible to identify a limited set of individual profiles that characterize the majority of the population. Statistical analysis has already been used to document "clusters" of qEEG features seen in populations of psychiatric patients. These clusters are considered here as intermediate phenotypes, based on genetics, and are reliable indices of brain function, not isomorphic with DSM categories, and carry implications for therapeutic intervention. We call for statistical analysis methods to be applied to a broad clinical database of individuals diagnosed with neurobehavioral disorders in order to empirically define clusters of individuals who may be responsive to specific neurophysiologically based treatment interventions, namely administration of psychoactive medication and/or EEG neurofeedback. A tentative set of qEEG profiles is proposed based on clinical observation and experience. Implication for intervention with medication and neurofeedback for individuals with these neurophysiological profiles and specific qEEG patterns is presented.

Neurofeedback is an emerging neuroscience-based clinical application, and understanding the underlying principles of neurofeedback allows the therapist to provide referrals or treatment, and provides clients with a framework for understanding the process. The brain's electrical patterns are a form of behavior, modifiable through "operant conditioning," with the excessive brain frequencies reduced, and those with a deficit are increased. The learning curve for EEG has been described (Hardt, 1975).

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The AJDC algorithm addresses limitations observed in AMUSE and outperforms it. No statistical difference is found between the manual and automatic approaches on a database composed of 15 healthy individuals, paving the way for an automated, operator-independent, and real-time eye-blink correction technique.