RigaBrain zinātne
Šajā sadaļā pamazām iepazīstināsim ar zinātni, kas saistās un ir par pamatu “RigaBrain” smadzeņu autotreniņiem.
Zināšanai – ”RigaBrain” ir SIA “RigaBrain” zīmols, zem kura tiek piedāvāti inovatīvi autotreniņi smadzeņu darbības uzlabošanai. Tehnoloģiskās pieejas mainās atbilstoši jaunākajām un progresīvākajām pieejām pasaulē. Kopš 2008. gada, kad “RigaBrain” uzsāka savu darbību, tehnoloģiskās pieejas ir nomainījušās vairākārt. Un SIA “RigaBrain” nepārtraukti seko tendencēm pasaulē, izvērtē tās un ieklausās savos sadarbības partneros un klientos, lai pats modernākais būtu sastopams “RigaBrain” kabinetā. Šobrīd tiek izmantotas kanādiešu un amerikāņu tehnoloģijas un pašas SIA “RigaBrain” uzkrātā pieredze.
Sniegsim mazu ieskatu par šobrīd izmantojamām tehnoloģijām SIA “RigaBrain” kabinetā.
SIA “RigaBrain” vadītājs Pēteris Urtāns aicina uz semināriem padziļinātāki iepazīties ar RigaBrain autotreniņiem no dažādiem skatu punktiem un zināšanu līmeņiem.
______
RigaBrain autotreniņu pamatā ir nelineārā neiroloģiskās atgriezeniskās saites metode NeurOptimal, un RigaBrain autotreniņu ietvaros smadzenes tiek izprastas kā kompleksa, dinamiska sistēma.
Visus šos terminus un skatījumus Jūs varat meklēt zinātnisko publikāciju datubāzēs. Tās pieejamas šeit!
______
Vairāk kā 4 500 zinātnisko publikāciju par neiroloģiskās atgriezeniskās saites metodi: Spied šeit!
Raksti par “nonlinear brain” – Spied šeit!
______
The Mean of the median: A New Metric for Targeting in Clinical Neurofeedback?
Valdeane W. Brown
Zengar Institute, Port Jefferson, NY
In order to explore the role of “variability” around targets during Neurofeedback, NeuroCare Pro® software was used to measure the emergent median values of each of the targets employed during Period 3 training, as well as in post-hoc analysis of other data. These median values were concurrently subjected to a 16 times per second averaging procedure to derive a dynamic measure of the “variability” of the emergent “central tendency” of each of the targets during clinical training. This “Mean of the Median” (or MoM) measure was used to derive the actual triggering of feedback events in re: to calculated divergence within a neighborhood around this dynamic measure. An 80% “inclusion” criterion was used for determining the size of the neighborhood for each target. For inhibit targets, feedback was enabled and/or produced when the emergent median remained within that neighborhood, whereas feedback for all targets was disabled by excursions beyond this neighborhood for any inhibit. The same basic procedure was used for augment targets except that excursions outside of an augment’s neighborhood had no effect on any other target. This procedure was used for active training of clients (N > 50), as well as for post-hoc analysis of pre-existing data obtained from other, successful Neurofeedback cases (N > 200) using different feedback paradigms, equipment and/or software. Several interesting results have begun to emerge from these preliminary investigations:
1- Clinical improvements appear to be correlated with decrease in both negative and positive divergence for all inhibit targets. This reflects a kind of “regression to the mean” re: inhibits during renormalization.
2- Renormalization of the inhibits may be an indicator of resilience in the CNS, or what Pribram refers to as efficiency.
3- Clinical improvements appear to also be correlated with a decrease in the number, duration and intensity of negative divergences for all augment targets but do not seem to be particularly correlated with any form of positive divergence for the same targets. Thus, it appears that it is not increases in augments per se that are important, but lack of decreases.
4- Renormalization of the augments may be an indicator of flexibility in the CSN, or what Pribram refers to as effectiveness.
5- This procedure may yield some useful metrics for successful training regardless of the approach used.
______
Non – linear Dynamics Panel: EEG, Neurofeedback and Non – linear, Dynamical Approaches: Explorations into the Chaos at the Cutting Edge of the Clinical Practice and Research
Valdeane W. Brown
Zengar Institute
Data acquisition and “real-time” analysis continue to be a central issue in the rapidly emerging field of Neurofeedback. Fundamental questions concerning the characteristics of the EEG signal itself directly affect equipment manufacturers, researchers and clinicians. These questions can not be avoided any longer – with the easy availability of advanced computing platforms and sophisticated statistical packages, the average practitioner can reasonably address these concerns within the confines of his/her own office. In this panel we will discuss many of the current issues concerning the role that Non-Linear, Dynamical or NLD approaches to data analysis play in the field of Neurofeedback. Chaos theory, as these approaches are also known, has been applied successfully to many other scientific domains including biology, economics, hydraulics, aerodynamics, cognitive science and meteorology. In fact, virtually every other field of inquiry has benefited greatly from the insights and techniques afforded by this revolutionary and essentially interdisciplinary approach to scientific inquiry. Thus, there is a clear difference in paradigm implied by a shift to NLD or Chaos Theory and the question really is: Is there any reason to make this shift in paradigm?
+
Because of all of this there isn’t a specific “target” or “profile” towards which (or away from which) we hope to direct the CNS. We don’t have a concept of the “normal” brain and try to get the brain to produce a “more normal” profile — that would be to base what we do on presumed normative data that actually doesn’t exist at this time.
All we do is to present real-time information to the CNS, in its own language (viz that of time-frequency atoms), and that allows the CNS to do what it does best: viz, process information and respond to that information.
______
Brain lives at “edge of chaos”
March 18, 2009
Courtesy Public Library of Science
and World Science staff
U.K. researchers are offering new evidence that the human brain lives “on the edge of chaos,” at a critical transition point between randomness and order.
The study, published March 20 in the research journal PLoS Computational Biology, provides experimental data on an idea previously fraught with theoretical speculation.
Scientists have identified a phenomenon they call self-organized criticality—where systems spontaneously organize themselves to operate at the borderline between order and chaos—in many different physical systems, including avalanches, forest fires, earthquakes, and heart rhythms.
According to the study, by a team from the University of Cambridge, the Medical Research Council Cognition & Brain Sciences Unit, and the GlaxoSmithKline Clinical Unit Cambridge, human brain network dynamics have something important in common with some superficially very different systems in nature.
Computational networks showing these characteristics have also been shown to have the best memory and information-processing capacity, researchers say: critical systems can respond quickly and extensively to small changes in their inputs.
“Due to these characteristics, self-organized criticality is intuitively attractive as a model for brain functions such as perception and action, because it would allow us to switch quickly between mental states in order to respond to changing environmental conditions,” said co-author Manfred Kitzbichler of Cambridge.
The researchers used brain imaging techniques to measure dynamic changes in the synchronization of activity between different regions of the functional network in the human brain. They also investigated the synchronization of activity in computational models, and found that the “dynamic profile” they had identified in the brain was exactly reflected in the models.
“A natural next question we plan to address in future research will be: How do measures of critical dynamics relate to cognitive performance or neuropsychiatric disorders and their treatments?” said Kitzbichler.
http://www.ploscompbiol.org/home.action
_______
The Science of Neurofeedback
The positive effects of neurofeedback were first noted by accident. Dr. Barry Sterman, a research scientist, was studying the levels at which toxic fumes caused cats to experience seizures. Dr. Sterman noticed that one group of cats was able to withstand much greater levels of toxic fumes prior to seizuring. Puzzling over the causes, Dr. Sterman noted that these cats had been reused from a prior experiment in which they had undergone neurofeedback training. Further studies revealed what Dr. Sterman had begun to suspect: neurofeedback training had a preventative effect on seizuring in both cats and humans. Many studies later, the evidence suggested that neurofeedback training was helpful for many conditions, such as attention deficit disorder (ADD), depression, improving cognitive focus, alleviating sleep disorders, etc.
Neurofeedback began to be used by psychologists for training the brain to alleviate symptoms such as seizures in epileptics and attention in those with ADD. Typically, sensors would be hooked up to a client’s brain in order to read and map brain activity. The client’s brain map would be compared to a database of average people of the client’s age and gender to determine areas of over- or under-activity, and then a course of treatment would be provided to reward activity in some areas and punish it in others.(1) Typically, rewards and punishments are focused on a few highly specific frequencies of brainwave activity. This means of conducting neurofeedback is still in use by most neurofeedback providers today.
In the 1990s, Dr. Val Brown began to doubt this mode of neurofeedback, feeling that rewarding and punishing a limited set of frequencies of brain activity might have more adverse than beneficial effects. He and Dr. Susan Brown, both clinical psychologists and neurofeedback experts, began to experiment with new ways of conducting neurofeedback. With highly sophisticated mathematics programmed into their neurofeedback system, the Drs. Brown found that clients’ brains, given information about their own functioning, would dynamically adjust themselves over time to more optimal functioning across a broad spectrum of brain frequencies. No diagnosis was necessary. Because no reward or punishment is administered in this system, and because a comprehensive set of frequencies is used rather than a few specific frequencies, the risk of side effects is low. Yet because the human brain responds automatically to information about its own functioning, clients undergoing the neurofeedback training provided by the Drs. Brown experienced significant enhancements in brain and central nervous system functioning. “Everyone can benefit from NeurOptimal™ because everyone has a brain and a central nervous system”, according to Dr. Susan Brown. Trainers claim that NeurOptimal™ is highly effective for a wide variety of disorders and people, and has been most effective with anxiety problems, stress related sleep disorders, depression, ADD/ADHD, headaches and migraines, and focus issues.
(1) http://en.wikipedia.org/wiki/Neurofeedback, accessed June 29, 2009.
_____
Viens uzskatāms piemērs par Neurofeedback metodes efektivitāti. Spied šeit!
_______
Probācija un cietumnieki - spied šeit!
Laimas slimība un CNS – spied šeit!
Neiroreakcija nemiera un afektīvu traucējumu ārstēšanai
D. Corydon Hammond, PhD, ABEN/ECNS , Physical Medicine and Rehabilitation, University of Utah School of Medicine, PM&R 30 No 1900 East, Salt Lake City, UT 84132-2119, USA
Evidence-Based Practice in Biofeedback and Neurofeedback 2008; grāmata – spied šeit!
____
______
______
