Adapting qEEG for the Brilliance Scan

by | Feb 5, 2023

We’re very proud of our Brilliance Scan, but one of the questions we get asked a lot from people who have a scientific foundation is, how does it all work?

And it’s a question that we struggle to answer simply, because the answer isn’t simple. It’s the result of thousands of hours spent in literature reviews, experimentation and optimisation with a multi-disciplinary team to get us to a stage where we were able to provide valid and reliable results. To try and give some insight into exactly what that process looked like, read on!

To understand where we are now, we need to quickly go back to the start.

The history of qEEG (Quantitative Electroencephalography) dates back to the late 19th century when scientists first discovered that electrical signals produced by the brain could be measured. However, it wasn’t until the development of the first EEG machine in the 1920s that the study of brain waves became possible.

Originally, qEEG was used primarily for medical purposes, such as diagnosing neurological disorders and monitoring the effects of drugs on the brain. Over time, its applications have expanded beyond the medical field and into the realm of peak performance, where it is used to help individuals reach their full potential. At Brilliance Lab, we’ve adapted qEEG to map, measure, and improve the cognitive, emotional, mental, and motivational factors that affect performance.

The process of measuring brain function with qEEG begins with the recording of electrical activity in the brain. Electrodes are placed on the scalp and the electrical signals produced by the brain are amplified and recorded. The resulting data is then analysed to identify patterns and trends in brain activity, providing valuable insights into brain function.

The adaptation of qEEG from a medical diagnostic tool to a tool for measuring performance the way that we do (which we call our P-blocks, or performance building blocks) has required a significant amount of research and development. In order to adapt qEEG for the measurement of P-blocks, several challenges had to be overcome.

One of the main challenges was developing a methodology for mapping the various cognitive, emotional, mental, and motivational factors to specific brain regions and frequency bands as a lot of these have previously been metaphorical schemas and constructs. This required a thorough understanding of the neural basis of each factor and how they are represented in the brain. For example, the relationship between stress management and the activity of the amygdala, a key player in the stress response, needed to be understood in detail. Similarly, the relationship between creativity and the activity of the default mode network, which is involved in mind-wandering and spontaneous thought, needed to be explored.

Another challenge was the development of appropriate algorithms for processing and analysing the qEEG data. The standard techniques used in medical diagnosis were not suitable for measuring P-blocks, as they did not account for the specific relationships between brain regions and frequency bands. This required the development of new algorithms that could accurately and reliably map and measure the P-blocks.

To achieve these goals, our team drew on the latest advancements in the field of cognitive neuroscience and biopsychology, as well as new developments in qEEG analysis techniques. What we do now, would not have been possible with technology from even 5 years ago. One of the key advantages of qEEG is its ability to provide objective and quantitative data about brain function. This data can be used to identify areas of the brain that are underperforming or overactive, allowing for targeted interventions to be designed and implemented.

When we began to experiment using qEEG to measure P-blocks, we had to consider the different frequency bands and brain regions that are associated with each cognitive, emotional, mental, and motivational factor. For example, the alpha frequency band is often associated with relaxation and mindfulness, while the theta frequency band is linked to creativity and intuition. The gamma frequency band has been linked to high-level cognitive processing and consciousness, while the beta frequency band is related to focused attention and alertness. Additionally, various event-related potentials (ERPs) can be measured through qEEG, such as the N400 component that is related to language processing, or the P300 component that is linked to decision making processes. To optimise the measurement of P-blocks using qEEG, advanced techniques like independent component analysis (ICA) or source localization are employed to isolate the specific brain regions and neural networks associated with each factor. By combining these techniques, we get a comprehensive view of the brain’s activity and its impact on peak performance, giving valuable insights into the relationships between cognitive, emotional, mental, and motivational factors.

We have used evidence based research and peer-reviewed models as the foundation to adapt the use of qEEG in peak performance to measure p-blocks by recording and analysing brain activity in response to specific stimuli, such as tasks designed to elicit a particular emotional response or to measure motivation levels. This allows for a more nuanced understanding of brain function and provides valuable information that can be used to design interventions to improve performance in specific areas. By providing real-time feedback about brain activity, this can be used to help individuals improve abilities such as concentration, creativity, and decision-making.

qEEG has come a long way since its early origins in the late 19th century. Today, it is an indispensable tool for Brilliance Lab to help individuals step into their full brilliance either personally, professionally or socially. With its ability to provide objective and quantitative data about brain function, qEEG has revolutionized the field of peak performance and has the potential to continue making a significant impact for years to come.

To see where it goes, watch this space!

Or, if you want to discuss it further or you’re a PhD student researching this area, please reach out to our CEO and Head of R&D Dave Morris on