There are two kinds of neurofeedback systems – linear and non-linear and there’s a difference between the two.
A bit about systems theory
Our brain is non-linear in its behavior, as are all organisms and the natural world. Eastern and indigenous cultures have been expressing this non-linear nature for thousands of years through their cultural stories, philosophical systems, religion and ritual, and, although quantum physics is probably the western equivalent, those of us from European cultures and professional education based on linear thinking, face significant challenges in grasping and describing complex, dynamical, adaptive systems acting in synergy. This is another way of describing dynamical, ‘non linear’ systems. There are assumptions so deeply embedded in the western world view and education that we are not generally aware of them. These assumptions, or accepted beliefs, make it difficult to understand and/or deal with system complexity. They include:
- every observed effect has an observable cause.
- even very complicated phenomena can be understood through analysis. That is, the whole can be understood by taking it apart and studying the components.
- sufficient analysis of a system, for example, a weather pattern or a living organism, can create the capacity to predict future behavior.
These assumptions have proven potent in developing our understanding of the physical world. They have served us less-well, however, in illuminating how weather patterns function, how human individuals and groups behave and how our brains function. Such complex systems are predictable in their general behavior but are notoriously impossible to predict in detail. This area of study was originally called ‘chaos theory’ and has now matured into a field known as “complexity science.”
Complex systems are highly composite ones, built up from very large numbers of mutually interacting sub-units (that are often composites themselves) whose repeated interactions result in rich, collective behaviour that feeds back into the behaviour of the individual parts (think of a large healthcare system, for example).
Another important feature of a complex system is the idea of feedback, in which the output of some process within the system is “recycled” and becomes a new input for the system. Feedback can be positive or negative: Positive feedback increases the rate of change of the variable in a certain direction and negative feedback reduces the rate of change in that direction.
In complex systems, feedback occurs between levels of organisation, micro and macro, so that the micro‐level interactions between the sub-units generate some pattern in the macro‐level that then “back‐reacts” onto the sub-units, causing them to generate a new pattern, which back‐reacts again and so on. This kind of “global to local” positive feedback is called coevolution, a term originating in evolutionary biology to describe the way organisms create their environment and are, in turn, molded by that environment.
Complicated systems can have very many parts too, but they play specific functional roles and are guided by very simple rules. Complex systems can survive the removal of parts by adapting to the change. The large healthcare system will still be robust with the removal of a single nurse because the rest of the members of the system will adapt to compensate—however, adding more nurses does not necessarily make the system more efficient. On the other hand, a complicated piece of medical technology, such as a positron emission tomography scanner, will obviously not survive the removal of one component.
A dynamical system is a system whose state (and variables) evolve over time, doing so according to some rule. How a system evolves over time depends both on this rule and on its initial conditions—that is, the system’s state at some initial time. Feeding this initial state into the rules generates a solution (a trajectory through phase space), which explains how the system will change over time. By feeding solutions back into the rule as new initial conditions, it is possible to say what state the system will be in at a particular time in the future.
difference between linear and non-linear neurofeedback
Linear neurofeedback usually relies on a medical expert diagnosis of a particular medical or mental health condition, such as, say, depression, and the development of a neurofeedback protocol to “uptrain” through a reward-system or “suppress” through a negative feedback system, certain frequencies in particular parts of the brain as treatment for that condition.
A QEEG, sometimes referred to as “brain mapping”, is a diagnostic tool that measures electrical activity in the form of brain wave patterns. In linear neurofeedback, a QEEG is taken of the person’s brain prior to treatment to work out where to uptrain or suppress. Any treatment protocol based on a QEEG brain map will therefore necessarily be addressing a fixed view of the brain’s activity, not how it is while the treatment is occurring.
NeurOptimal® neurofeedback systems are diagnostically agnostic and are currently the only non-linear, dynamical neurofeedback systems which feed back whole brain electrical activity in real time at 256 times per second and not requiring operator input. The only factor limiting the speed of the feedback is the computing power available in a tablet/laptop format.