EEG Research

TaKeTiNa and Neuro-vegetative Rhythmicity

Dr. Michael A. Überall, Medical Director
Institute for Neurosciences, Algesiology and Pediatrics – IFNAP

EEG Measurements

Dr. Michael Überall and his team have analysed EEG results taken from a TaKeTiNa research workshop held over a period of several days in May 2010 at the TaKeTiNa Institute in Vienna. Dr. Überall and his team measured participants’ brain waves before, during and after the TaKeTiNa exercises.

QEEG pilot study shows objective changes of bioelectric brain activity as a result of TaKeTiNa

Quantitative event-related analysis of bioelectric correlates of cerebral activity has long been part of human medical research extending traditional therapeutic indications (e.g. in epileptology). Thanks to modern high-performance computing systems and complex mathematic algorithms, these techniques not only lay the foundations for a new neurological understanding of the relevance of EEG signals, but also open up entirely new perspectives for complex psychological questions and alternative therapeutic approaches. However, despite improvements of these approaches, especially in areas considered problematic by academic medicine such as chronic pain etc., methodological inadequacies have made verification of the results difficult, explaining why they play no significant role in current medical practice, or at best an outside one.

The remarkable success of TaKeTiNa especially in connection with refractory chronic pain syndrome led to a call for new research. A pilot-study was carried out in summer 2010 at the TaKeTiNa Institute in Vienna. Neurophysiological correlates of cerebral brain activity were recorded using multi-channel electrodes in participants attending a TaKeTiNa workshop and were then analysed.

Our a-priori hypothesis was that:

a) certain chaotic phases experienced by participants repeatedly during a polyrhythmic TaKeTiNa journey are of central importance for the development of new endogenous coping strategies to manage chronic illnesses. These should be objectifiable and verifiable via any corresponding neurophysiological correlates (e.g. sudden changes to the frequency bands typical for trance, hypnagogic consciousness, waking dreams, hypnosis, meditation, deep relaxation and increased learning ability).

b) the time and duration of these phases should be clearly definable using QEEG analysis.

c) QEEG changes associated with these phases should be more pronounced than initial values measured before the exercise phase, i.e. when participants were in a state of complete relaxation.

d) these changes should neither spatially nor temporally correspond to other known QEEG changes (e.g. vigilance-dependent or medication-related).

The mathematical procedure used was a variation of the multichannel quantitative DAS time series trend analysis which translates the spatio-temporal and energetic characteristics of the morphological dynamics of the real time EEG into a biometrically unique macro-indicator for the brain. This enables a stochastic measurement (in analogy to the theory developed by Selye in the mid-twentieth century for the general adaptation syndrome formulated as integrative EEG-vigilance) and the objective quantification of specific physiological functions within the framework of an integrative concept.
We were not only able to clearly identify these chaotic phases, i.e. “falling out of the rhythm”, within the EEG traces, but also to identify bioelectrical resting states greater than those achieved by participants immediately before the TaKeTiNa journey (see example below). In addition, the experiences gathered from the rhythm journey initiated an autonomous learning process that not only improved the ability of participants to integrate these unconsciously, but also empowered them to utilise them in well-directed ways – e.g. during the resting period of subsequent follow up EEG measurements – (see differences in the measurement phases of rest before and after the actual exercise phase).

This is the first time that modern measurement and computer procedures have found evidence for TaKeTiNa-induced neurophysiological changes in cerebral brain electrical activity. We hope that these studies will become the foundation for an explanatory model for the remarkable effects of this rhythm-based therapeutic concept and also the starting point for completely new TaKeTiNa-supported biofeedback therapies.


Graph of the energetic rhythm of the QEEG macro-indicator before, during and after a typical 90-minute TaKeTiNa rhythm journey. Note the relaxation marker during the resting phase before the actual exercise (characterised by the significant red colour), the clear tension/activity during the polyrhythmic rhythm journey (strong red colour), the sudden onset of four phases of deep relaxation each associated with “falling out of rhythm” events (block-like blue/green phases) as well as the final relaxation marker during the final resting phase (significant less red/yellow colour compared to the initial situation).