Our brain activity are in constant flux and fundamental aspects of the dynamics vary both across the brain and over time. For some people this variability is small, for others it is a lot yet we know little of what it means about our individuality.
The brain’s electrical activity is constantly in flux – whether or not you think you are actively doing something or are even aware of what you are doing. Every aspect of the brain is never fully engaged in any conscious task at hand and the task specific activity therefore sits embedded in this larger sea of flux. How variable is this flux within an individual? This is an important question both from the perspective of understanding the ebbs and flows of our mind and also from how we construct and evaluate the dynamical correlates of specific tasks.
Here we will show you a glimpse of spatial variability from a sample of a few hundred people and temporal variability in 20 people from whom we measured 3 minutes of resting eyes closed activity in 10 separate sessions conducted over two weeks using the Emotiv EPOC.
See related post Is the Emotiv EPOC Signal Quality Good Enough for Research?
There is a myriad of ways in which we can look at this. However, we will talk here about two different types of measures of EEG activity: Complexity of the signal, which varies a little and Alpha Oscillation Energy, which vary a lot.
Defining the measures
Complexity, as we calculate it, is a measure of the diversity of complex waveform patterns in the EEG signal (as opposed to a measure of spectral complexity which, by transforming the signal into its frequency components, does away with the complexity conferred by the relative phase positions). You can read more about it here and here.
Alpha Oscillation Energy, as we calculate it, is a measure of the energy of only the oscillatory component in the alpha range and NOT the entire alpha band activity which is not an oscillation or wave but rather embedded in larger complex structures. You can read more about it here and here.
We compute both measures over the entire 3 minute duration of the recording.
Spatial variability refers to how the signal varies across different regions of the brain at the same time. The Emotiv EPOC has 14 electrodes that cover O1, O2, P7, P8, T7, T8, FC5, FC6, F3, F4, F7, F8, AF3, AF4. Turns out that in some people, these metrics are very uniform across brain regions while in others they vary a lot. The average variability of signal complexity (called CT in the figure below) across all regions was 10% but there was a fair bit of spread. Some people varied as little as a 1% percent while about 10% of the sample had large variability in the range of 20-35%. This means that for some people all regions of the brain produce an equal complexity of patterns whereas in others this is quite lopsided with some parts of the brain being very complex while others are not at all. One can speculate about the meaning of this for the individual and how it matters for mental and cognitive function. Is a person with equal complexity over space using different faculties more equally while lopsided folks are also lopsided in their types of information or sensory modalities they process?
Spatial variability of the alpha oscillation is even more stark. Indeed, in some folks the eyes closed alpha oscillation is present in all channels, while for others it is only detected in the occipital lobe. Generally, when there is an alpha oscillation, it is always in the occipital lobe (O1 and O2) but rarely, in a handful of people, the oscillation can be detected in alternate channels in the frontal lobe even when there is no occipital oscillation. If you include the channels without an oscillation, the spatial variability among our sample extends over a range of 20% to 250% with the largest number of people around 100%.
What could this mean? Alpha oscillations have been associated with mental imagery. Does this mean that people engage modalities differently in their mind wandering? Does it have bearing on how individuals process information? We have not computed phase synchronization of the alpha oscillation across regions, but perhaps this will shed some light on how people differently integrate activity. There is much to understand still.
Here we looked at the variability of the average across all channels over 10 snapshots in a 2 week period. The variability in complexity over time was similar to the variability in space. Half the people clustered very close to 10% but some were as high as 20% and others as low as 1%. In contrast, the variability in average alpha oscillation energy across trials was much lower than the spatial variability across channels. The average variability across trials was 50% with a maximum of 80%. Thus some of us stay more steady over time in the meanderings of our brain activity while others, simply put, are all over the place. This could relate to a number of things, the flexibility or rigidity of our mental processes or our capacity to change perhaps. It all waits to be tested.
Most studies in the literature do little to account for this individual background variability. Indeed with many studies touting differences in activity of 10% before and during a task are not taking into account the natural variation. The statistics used to acquire the p<0.05 make the implicit assumption that any variability is only due to the task and not natural fluctuation. For example, with a natural variability of 50% among individuals in the alpha activity, any study that utilizes alpha band as a metric and shows a 10% differences in a sample of 20 to 50 people could be way off. To know if one’s result is really due to the task and not other intrinsic fluctuation you would want to measure and account for the individual’s resting variability. One bootstrapping work around could be to use average numbers taking a 50% variability of alpha variability to introduce jitter and calculate probabilities of a significant difference. A relook at studies with this may well give us a very different view of results.
See related post Human Brains and the Control Issue
Accounting for and Celebrating our differences
The larger message, of course, is that we are all different in our brain dynamics and it behooves us to make the effort to understand these differences – what they are and what they mean, and celebrate our diversity instead of trying to average them away.
See related post The Myth of the Average Brain