Neurofeedback protocols - the foundations

Electrophysiological techniques like electroencephalography (EEG) can be used to characterise the brain's activity and relate it to function; however this is simple to say and challenging to do.  With over a hundred years of science and technical development behind them, systems to capture and process EEG such as the NeXus range are now robust and their operation is getting closer to “plug and play”.  Capturing data is only part of the story though - how do we derive meaning from the data?
Conventional EEG is fundamentally a qualitative tool and If we were to look at the raw EEG from one site on the scalp we would see something like this.

A sample of raw EEG from one site on the scalp

A sample of raw EEG from one site on the scalp

The magnitude (Y axis) of the EEG signal is likely to be in the microvolt range. The horizontal axis is plotted as Time, and so at a glance the raw EEG signal looks chaotic and it is difficult to recognise patterns that reflect subtle issues. 

Often, clinicians who have learned to work with EEG will examine multiple points on the scalp simultaneously; most often the International 10-20 system is used.  This is an approach that identifies specific locations on the head from which EEG measurements are taken.  This system identifies locations used to place electrodes. These locations can then be identified for example as C3, CZ, C4 and so on.  If the casual observer finds it difficult to recognise patterns from one EEG channel you can imagine the task is much more challenging when we have 21 channels to review simultaneously.

International 10-20 system of electrode placement

International 10-20 system of electrode placement

Following a standardised approach in EEG electrode positioning and processing assists research and clinical practice. To make the data capture easier there are a variety of caps and approaches. In the NeXus range for example, we have a full cap system, a minicamp, microchip and a Headset. The choice of electrode cap depends on the intended application with a full cap system used for qEEG and the minicap, microcap and Headset used for neurofeedack (EEG biofeedback)

However, as we said above, the issue is not just data capture - it's how to derive meaning from the data.

The raw EEG signal would be described by an engineer as a “time domain” representation of change.  As we will see shortly, for our purposes we will most often want to process the raw EEG signal to show this change behaviour in what is called the frequency domain. This transformation is designed to elegantly "reveal the secrets" embedded in the EEG and it is founded on the mathematics of the Fourier Series in which arbitrary periodic signals can be (approximated) represented as sets of sine waves or cosine waves of different magnitudes and frequencies. Even in the early days of Hans Berger's exploration of the EEG in 1929, the patterns of change seen in the raw EEG were broken down into sets or bands of frequencies that became known as delta, alpha, beta, theta etc.

One of the metaphors that is sometimes used to explain this Fourier series approach is to think of the way that white light can be split into it's component colours by a glass prism. Each colour from Red through to Violet emerging from the prism has a different character through what we know as its frequency spectrum.  If we imagine that the original EEG signal we are processing is our "white light", the Fourier Series approach acts like a prism and identifies the component sine or cosine waves of different magnitudes and frequencies (the metaphorical colours). It's like finding the constituent parts of the signal so that if we added these parts together we would recreate the original.

Why bother?  Well the significance of this is that instead of looking at (in theory) an infinite amount of this rapidly changing raw data we now see it represented in a more concise form as a frequency spectrum.  With modern hardware such as the NeXus systems the raw EEG is sampled very quickly and processed using the Fast Fourier Transform technique so that the user can see both the raw (time based) data and the frequency spectrum at the same time if they choose. From the early days of EEG exploration the amount of energy found within different frequency bands was given a name - for example Beta for frequencies between 13 and 30 Hz and so on.

Transforming raw EEG into frequency bands

Over the years of clinical and scientific exploration the patterns observed through the amounts of energy seen in each of these bands has become associated with various brain states.

Frequency bands in an EEG signal capture

It is important to distinguish the examination of raw ‘EEG’  from the related areas of Neurofeedback and quantitative EEG (qEEG).  Neurofeedback is a form of biofeedback training that uses the EEG signal to control what is provided as “feedback” to an individual whose EEG is being measured.  The aim of this is fundamentally to enable beneficial change in brain states.  Despite the fact that it is not yet widely practiced in the UK, neurofeedback is a strongly evidence-based approach with extremely solid scientific foundations.

QEEG meanwhile is a technique in which the raw, captured, EEG signals are post-processed by computer to produce various analytical measurements - often by comparison with a database of “norms”.  These norms can be used for example to guide clinical decision making.  QEEG can also be used to guide treatment progress. Despite the fact that EEG, qEEG and Neurofeedback are based on the same underlying signals (sometimes referred to as “brain waves”) they are actually based on different sets of assumptions and have different clinical purposes.  Neurofeedback is best used when it takes advantage of brain plasticity to support and reinforce clinical goals in a manner consistent with evidence based practice. Neurofeedback is not a quick fix or “one size fits all intervention” but is nevertheless often an effective tool to bring about beneficial change.

Neurofeedback has evolved over more than 30 years of clinical practice and certain protocols have been accepted as "standard" in the sense that their effects and clinical roles are well understood.

Amongst these protocols are - Alert C3, Deep Pz, Focus C4, Peak C3-C4, Peak 2 C3-C4, Relax Oz, Sharp Cz. We will examine these in more detail in future articles but let's consider one as an example.

Setting the feedback threshold in Biotrace+ software

The Alert protocol is widely known as Beta training. In this protocol, the client typically has EEG electrodes placed at C3 or CZ on the scalp.  In an ideal scenario the operator is looking at a screen (see above) which gives access to the data in the time domain and the frequency doman (frequency bands).  The operator can than choose to activate feedback for the client who is looking at a second screen. The operator can set for example to enable feedback if the amount of energy in a particular frequency band is greater than a threshold level.

The operator and the client screen running Biotrace+

The type of feedback provided to the client can vary greatly but in this case would be set to encourage the development of more Beta energy.  This is basically one of the historical protocols used for ADD/ADHD dating from the mid 1980's. Although we have described this as a protocol which implies a straightforward process, there are some wider considerations in practice.

We will explore more of these protocols in a future article.

More reading? - See Collura, TF. "Technical Foundations of Neurofeedback".Routledge Press 2014