Lab Talk

Complexity of the EEG reflects Life Context

The complexity of our brain dynamics as measured by EEG reflects the complexity of our experience.

Modern Life is more complex

When was the last time you flew in an airplane, took a train or drove a car? Quite recently I imagine. They are all modes of transport that many of us now take for granted. A modern way of life that has become ingrained into how we get from A to B.

But as you look out the window and see the world race past, have you ever thought about the fact that your brain might have had to evolve to keep up? The rate of visual stimulation when you travel at speed is far greater than that required by the age old act of walking, or even galloping on a horse, from place to place. Besides, being able to travel faster allows you to explore further and therefore create a much larger geofootprint, experiencing a wider range of physical and cultural environments. What this means is that modern advance increasingly allows for a far more diverse range of experience.

The brain configures itself in an experience dependent manner. What then is the impact of modern advance on our brains? Do they produce a greater complexity of patterns that mirror the greater complexity of experience, adapt new mechanisms of handling the faster and richer stimulus environment to extract meaning?

Dimensions of diversity

Studies have shown how environmental enrichment and socioeoconmic status can impact of the neurobiological structure of the brain, and these are both related to access to modern tools and amenities. However, the true impact of “modernity” is still not clear. We are a diverse species that have changed our world profoundly in the last 200 years and in the process have diverged further and further in our life contexts and individual experiences. Today humanity spans a wide range of experience from hunter-gatherer tribes to small self-sustaining agricultural communities to small town living and high speed digital urban life. Who is the ‘average’ human and is there such a thing anymore?

This is the essence of the Human Brain Diversity Project. And in a first project and dataset, Sapien Labs has made a start at understanding the impact of modernity and life context on the human brain by comparing the EEG activity from individuals across diverse sectors of humanity.

The study, which was conducted in the Tamil Nadu region of India, sampled activity 402 people across 48 different locations, ranging from large metropolitan cities through to remote rural communities of only 300 people. In each location resting brain activity was measured whilst participants sat for 3 minutes with their eyes closed using the 14 channel Emotiv EPOC wireless EEG headset.

As well as collecting this EEG data the study also profiled the 402 participants to obtain a range of demographic metrics such as income, education and geofootprint, as well as taking a record of their fuel, electricity, mobile phone and internet usage.

A measure of brain complexity

Rather than focusing on spectral properties (read The Blue Frog in the EEG) or specific scalp regions, the study took a whole-brain approach and devised a metric to measure the temporal complexity of the EEG signal from each individual.

In essence, this measure of complexity (which by definition can range from 0-100) reflects the number of different waveform patterns observed across the scalp within a particular window of time, representing the temporal complexity of the underlying neural activity. A score of 0 would be a flat line or nearly unchanging pattern while a score of 100 would represent a structured non-repetitive pattern of activity in the period of measurement.  It is fundamentally different from but nonetheless related to spectral and wavelet entropy measures.

Within the experimental sample, the measure of EEG signal complexity ranged considerably from 35 to 96, and was shown to include a broad range of spectral frequencies above 4Hz. In addition, this measure of complexity had cognitive relevance as it was shown to be highly positively correlated with scores on a pattern completion task.

Income, Education and Geofootprint make a difference.

Three main factors emerged as having the most significant relationships to brain complexity: education, income and geofootprint. The data showed an approximately linear correlation between education level and brain complexity. In contrast, income showed a logarithmic relationship increasing steeply until an income somewhere in the range of $30-$50/day at which point it leveled off.

Complexity vs Context Factors

But it was geofootprint, a composite measure which took into account the geographical distance of travel during the previous year; which showed, the strongest and most surprising relationship with brain complexity. Those with a geofootprint that extended outside the country showed significantly higher levels of neural complexity. Furthermore this benefit was still present even when controlling for the contributing factor of income, suggesting that it goes beyond just the expComplexity vs Composite Contexteriential opportunities afforded through financial gain. And potentially reflecting the fact that the brain has had to adapt to “keep up”.

 

Finally however, rather than any individual demographic factor, it was the composite of complex modern experience (principal component scores) that was most systematically related to the complexity of the EEG signal indicating that brain dynamics are conditional on context.

 

 

 

A New Normal

As one of the first forays into trying to understand the brain correlates of human cognition across different sectors of humanity, the results are startling. Living a modern way of life make an impressive imprint on our underlying brain activity.

Furthermore, the idea that a “normal” EEG is the one that is observed in a university research laboratory is no longer a valid assumption. The possible range of activity is profoundly dependent on life context.

Brain diversity is the new normal.

The paper on BioRxiv can be found here:

Complexity of Human EEG Reflects Socioeconomic Context and Geofootprint

 

 

2 thoughts on “Complexity of the EEG reflects Life Context

  1. It is an interesting paper where some general conclusions are made, based on the analysis of EEG data collected over populations whose income varied widely. It is based on a new measure of complexity which employs the cross correlation at zero lag between non overlapping segments of an EEG time series. A question for which I would like an answer is why introduce a new complexity measure when there are several existing measures which have been extensively investigated for a range of different time series. For example approximate entropy, sample entropy, shannon entropy, Renyi entropy are some of the complexity measures which have been used in EEG studies. Is there any special feature present in this new measure of complexity?

    1. The fundamental difference in this measure over others is the longer timescale, which seems to be very important to the relationship to contextual factors. You could think about this using this analogy – if you were to assess the complexity of a page/string of text using a complexity measure with a short length scale on the order of letters you would get a very different result from a measure that considered a length scale on the order of words or sentences since complex words or sentences may nonetheless use a similar complexity of letters to simpler words or sentences.

      We are looking at a systematic comparison of this measure of complexity to other measures and will publish this sometime during the year. Meantime we have shown a comparison of this measure to spectral entropy in the paper to which there is a positive, although nonlinear relationship.

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