EEG Alpha training is put in the context of the entire brain, and its normal level of cycling. This can help to motivate neurofeedback training, and connectivity training in particular.

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It is common to regard the alpha rhythm as an idling rhythm, one that takes place when no active work is done.  This is taken in contrast to the faster rhythms such as beta and gamma, which are considered to represent a "processing" phase.  Thus, the concentration/relaxation cycle is taken to be that characterized by phases of relaxation (alpha) and phases of concentration (beta).  The use of alpha enhance protocols, and the use of "squash" protocols, both appeal to the notion of this cycle, and seek to produce either a preponderance of one phase, or the facility to alternate flexibly between phases.

The general observation that alpha amplitude is suppressed during tasks, has caused a view that alpha is solely a 'relaxation" mechanism, and does not participate in information processing.  However, it is now evident that the alpha mechanism is not simply a pacing or timekeeping mechanism.  Rather, it participates in memory scanning and related mental processing in a profound manner.  Contrary to the concept that thinking occurs when alpha is absent, it is more correct to say that alpha activity persists during essentially all mental processing, but that it takes on a character of desynchronization during certain tasks.  It is the desynchronization (independence) at a cellular level that produces the amplitude reduction, not any decrease in the "alpha" activity at the individual level.

The mechanism that produces synchronous alpha also operates at the cellular level, as the relaxation of inhibitory fibers whose primary function is to throttle the activity of the active (pyramidal) cells.  These are primarily thalamocortical (collateral afferent) and cortico-cortical projections, that hold back the pyramidal cells from firing, despite the considerable excitatory input they may see.  These receive information including sensory, body awareness, muscle feedback, and related signals, impinging on a range of primary sensory, secondary sensory, and somatosensory areas.

There is also a mechanism involving the reticulothalamic modulation of thalamocortical activity, via inhibitory projections from the reticular activating system into the thalamus.  Training of alpha rhythms also likely affects this mechanism, as part of the brain's overall strategy toward satisfying the training goals.

Timing is what is controlled.  At any given time, pyramidal cells are likely to be firing or not firing, as a function of their local population dynamics.  But when the firing begins to synchronize, that alone is sufficient to increase the amplitude of the surface waves 2, 3, or more times.

Alpha is always there at a cellular level.  However, the memory scanning that it represents is modulated at a unit-cell level, so that the relative timing of neighboring cells is modulated.  Desynchronization and synchronization is what is modulated.  Thus, the pyramidal cell populations can slide in and out of phase with each other, providing a wide range of surface amplitudes, which we see as EEG.

The highly synchronized (high alpha) state is a low entropy state.  The more synchronization can be achieved, the lower the momentary entropy of the system.

Entropy can be sequestered in time as well as in space.  The time-space analog of a highly organized, crystalline material is the synchronous alpha activity in the brain.  Rather than simply organizing matter in a structured fashion, the brain organizes events in a structured fashion.  When alpha goes out of phase at a regional level, so that there is an interface of phase change, then the flow of entropy from one region to another is maximized.

This provides a rationale for connectivity-based training.  By considering the various connectivity pathways in the brain, and training them explicitly for flexibility, it is possible to use a wide range of protocols toward a primary goal, without resorting to a simple model of "remediating what is too large or too small".

This also provides a rationale or downtraining, in the context of connectivity.  The effect of downtraining a rhythm is to exercise the inhibitory influences, in such a way as to induce them to produce maximal desynchrony, hence independence.  When viewed as an essential component in the momentary switching of mental tasks, this flexibility can be expected to lead to enhanced mental fluidity and effectiveness.

Alpha training thus provides functional relaxation, not systemic relaxation.  That is, the mechanism of relaxation, which performs at a cellular level, relaxes internal inhibitory influences, thus resulting in an increase in synchronized, aggregate activity of active mental processing elements.  Although these elements are not necessarily engaged in a task at the time time of the training, the resulting learning provides flexibility of activation, which provides benefits at future times.

Authors Bio:
Dr. Collura has over 30 years experience as a biomedical engineer and neurophysiologist.  He has conducted clinical research and development and system design, in the areas of evoked potentials, microelectronics, human factors, EEG mapping for epilepsy surgery, and neurofeedback.  His graduate work focused on the real-time measurement of visual and auditory evoked potentials, and relationships with selective attention in a vigilance task.  He then spent 8 years with AT&T Bell Laboratories as a technical staff member and supervisor in the areas of integrated circuit technology, computer graphics, networking, and man/machine interfaces.  He then served from 1988 to 1996 on the Staff of the Department of Neurology, Cleveland Clinic Foundation, where he conducted research and development in EEG mapping for epilepsy surgery, long-term EEG monitoring, and DC brain potentials.  As a consultant to industry, he has designed software and hardware for computerized tomography, automated radiometry, and automated imaging.  Since 1995, he has been founder and president of BrainMaster Technologies, Inc.  He has published over 100 peer-reviewed journal articles, book chapters, abstracts, and papers.  He has 2 patents and 3 patents pending, all in the areas of neurofeedback, electrode technology, and evoked potential methods and systems.  His current interests focus on research and development of automated neurofeedback systems, evoked potential neurofeedback, and low-cost quantitative EEG.  He is a licensed Professional Engineer in Ohio and Illinois, is a past board member of the International Society for Neuronal Regulation (ISNR), and is president-elect of the Neurofeedback Division of the Association for Applied Psychophysiology and Biofeedback (AAPB).