Most of the general public are familiar with brainwaves. However, a very small minority are aware of the uncertainty that prevails in the electrophysiology field. Ever since the first recorded signal by Hans Berger (Berger, 1929), alpha waves, most prominent in the visual cortex, we have been bewildered as to what they are. Mathematically minded researchers have been enjoying their time investigating behavioural and physiological correlates of brainwaves or oscillations, as they are referred to in the field. Nowadays, plenty of companies and researchers suggest stimulating different brain waves to improve this or that. Let’s dig at the field a bit and see what we find.
In 1929, Hans Berger published a paper where he reported EEG recordings of human brain waves at ~10Hz. They did not know what these waves did, which they called Alpha waves. They are very easy to observe, with a simple setup you can record them. They appear on the back of your scalp (where your visual cortex is) when you close your eyes or are very tired. Meaning, there is a negative correlation with function. They appear when you do not see anything.
It took some time before it was accepted as a “real” phenomenon. Researchers thought initially that it might be an artifact. Fast forward a few decades and we discovered that neurons can actually behave in this oscillatory manner without external input. Meaning that it can be an intrinsic property of the cells (Llinás, 2014). This means that by default, networks could oscillate for some unknown reason.
Oscillations could be a functional network behaviour, a biophysical property of the system, or a ”readiness” to fire when necessary. As synapses are highly dynamic, it may be necessary for neurons to stay sensitive for inputs by oscillating in this manner. However, these are speculative ideas at this point.
More recently, researchers have suggested a model that describes possible function through a mechanism involving coherence (Fries, 2005). Let’s say you have two populations of cells in different structures, one in Prefrontal cortex and one in visual cortex. Now let’s say both have an intrinsic property to oscillate membrane potentials at 10Hz (alpha). When these two populations synch by aligning their oscillatory phase, where the neurons are in an excited state, they can easily communicate. When they are in a depressed state, they cannot. That means that oscillations are a computational function in the brain, aimed at regulating communication pathways between networks. That is an elegant hypothesis. The problem is that we need data to support this and research on this topic reveals mixed results. A definitive answer regarding gamma oscillations causal implication for instance still lies ahead of us (Fernandez-Ruiz et al., 2023; Ichim et al., 2024).
Within the field of electrophysiology, most are familiar with György Buzsáki, who wrote the book Rhythms of the Brain (Buzsáki, 2006). In there, we find a description of an orchestra of neuronal computational mechanisms, involving inhibitory neurons regulating network activity, looped connections, and hierarchical communications through distinct oscillation coupling. All of this, correlating with memory and cognitive function. But are we at the stage where we can state with confidence that oscillations are the mechanisms driving these functions?
More recently, we have observed how electrical fields around active neurons might actually affect neighbouring cells. Creating waves of electrical fields in the tissue (Fröhlich and McCormick, 2010). This means that oscillations could arise as a result of network architecture or tissue organisation and not necessarily for computational function. So if you induce or reduce oscillatory activity, it may not alter the behavioural output of the network, because the oscillations are not a functional network behaviour but an emergent property of the organisation.
Another issue is the methodological approach involves potentially a lot of interference from distant signals, for example through volume conduction (Kajikawa and Schroeder, 2011). Where, waves of electrical fields from distant tissues travel to the probes implantation area. Researchers might think they are recording an oscillations in a mouse hippocampus, meanwhile the signal is diffusing through the tissue originating from some other areas.
The problem in this field is that there is a lot of correlational studies showing a positive result. Oscillation A plus behaviour 1 happen at the same time. This does not mean that Oscillations A caused behaviour 1. They merely happened at the same time.
Although we find many studies showing possible functions, only a few show direct causal relationship between oscillations and behavioural functions (Herrmann et al., 2016; Van Bree et al., 2025). Meaning, studies that regulate oscillations to observe effects on cognitive/behavioural outputs. However, more are starting to emerge out of model organism research.
In conclusion, whether brainwaves have a function or if they are an epiphenomenon is not yet answered in my opinion. We should be careful when addressing brain waves and attributing functions, as we are yet to have enough empirical evidence to cover the truth behind that claim.
Declaration of AI use: The author used Lumo AI for spelling, grammar-checking and proofreading during the preparation of this article.
References
Berger, H., 1929. Über das Elektrenkephalogramm des Menschen. Archiv f. Psychiatrie 87, 527–570. https://doi.org/10.1007/BF01797193
Buzsáki, G., 2006. Rhythms of the Brain. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195301069.001.0001
Fernandez-Ruiz, A., Sirota, A., Lopes-dos-Santos, V., Dupret, D., 2023. Over and above frequency: Gamma oscillations as units of neural circuit operations. Neuron 111, 936–953. https://doi.org/10.1016/j.neuron.2023.02.026
Fries, P., 2005. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends in Cognitive Sciences 9, 474–480. https://doi.org/10.1016/j.tics.2005.08.011
Fröhlich, F., McCormick, D.A., 2010. Endogenous Electric Fields May Guide Neocortical Network Activity. Neuron 67, 129–143. https://doi.org/10.1016/j.neuron.2010.06.005
Herrmann, C.S., Strüber, D., Helfrich, R.F., Engel, A.K., 2016. EEG oscillations: From correlation to causality. International Journal of Psychophysiology 103, 12–21. https://doi.org/10.1016/j.ijpsycho.2015.02.003
Ichim, A.M., Barzan, H., Moca, V.V., Nagy-Dabacan, A., Ciuparu, A., Hapca, A., Vervaeke, K., Muresan, R.C., 2024. The gamma rhythm as a guardian of brain health. eLife 13, e100238. https://doi.org/10.7554/eLife.100238
Kajikawa, Y., Schroeder, C.E., 2011. How Local Is the Local Field Potential? Neuron 72, 847–858. https://doi.org/10.1016/j.neuron.2011.09.029
Llinás, R.R., 2014. Intrinsic electrical properties of mammalian neurons and CNS function: a historical perspective. Front. Cell. Neurosci. 8. https://doi.org/10.3389/fncel.2014.00320
Leave a Reply