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How Volume Conduction Affects Your EEG Data

Understanding volume conduction is critical to interpreting your EEG data correctly. In this blog post, we’ll consider what volume conduction is, its relevance to EEG, and how you can address its complex effects on brain signals.

For much of this post, we will be referring to the wonderful chapter on volume conduction written by Seward Rutkove in The Clinical Neurophysiology Primer (Rutkove, 2007). So for a more in depth commentary, please consult that!

Volume conduction

Volume conduction (or “electrical spread”; Holsheimer & Feenstra, 1977) occurs when you measure electrical potentials at a distance from their source (Rutkove, 2007). It is the interference that happens between the source of an electrical potential and the electrode measuring that potential.

In general, there will be some medium that fills the space between an electrical source and an electrode. Volume conduction occurs because electrical signals conduct through this medium, which could be biological tissue.

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For instance, when we place electrodes on the scalp to measure brain activity, there are layers of skull and cerebral fluid between the neurons and electrodes.

These tissues conduct the electrical signals, causing them to spread and refract. This potentially alters the appearance of the signal once it reaches the electrodes (Rutkove, 2007).

In simple language, volume conduction means that electrical signals do not travel in a straight line from source to electrode.

Importantly, all EEG recordings suffer from the effects of volume conduction (van den Broek, 1998).

Volume conduction and EEG

Imagine a cityscape at night, where each building represents a different part of the brain emitting light. Now imagine trying to capture the exact shape and intensity of each building’s light from a distant vantage point.

The brain’s electrical signals are like those lights, and the “distance” between the brain and the electrodes introduces complexities in recording accurate signals.

A nighttime city in fog
The problem of volume conduction is like the problem of trying to see distant lights in a foggy city.

In fact, we can extend this analogy further by considering a fog that descends between you and the buildings. This is like the skull and cerebral fluid. We cannot be sure which electrical signal applies to which group of neurons.

Near-Field and Far-Field Potentials

Two key terms in the context of volume conduction are “near-field potentials” and “far-field potentials” (Rutkove, 2007).

Near-field potentials are signals recorded relatively close to their source but not in direct contact with it. They tend to be captured using bipolar recording setups.

In bipolar setups, researchers use two recording electrodes and a third, ground electrode (Beck et al., 2007). They measure the difference between the two “recorded” signals, relative to the ground electrode.

Bipolar, near-field potential measurements are commonly seen in needle EMG, where a needle electrode is inserted into a muscle to measure innervation (electrical activity; Rutkove, 2007).

Far-field potentials are signals recorded at a distance from their source. They play a substantial role in somatosensory evoked potentials: the signals described by EEG data.

If you’re interested in visually evoked potentials, check out our post on SSVEPs!

Naturally, far-field potentials, and by extension, EEG, suffer most from volume conduction.

Challenges faced by EEG researchers

Volume conduction presents a number of challenges for EEG researchers. In particular, the electrical signals from different brain regions can interact, leading to altered signal morphologies (Rutkove, 2007). This can result in researchers misinterpreting neural activity.

For instance, a signal originating from one area will be affected by its interaction with signals from other areas.

Moreover, the conductivity of head tissues can vary widely across individuals (Wolters & Munck, 2007). In fact, even within the same person age and disease, amongst other factors, affect tissue conductivity.

This adds another layer of complexity, as the conductive properties of the tissues influences the spread of electrical signals.

C1 may not mean C1

To summarise, and this is critical: just because you receive a signal at electrode C1 following some stimulus, it does not mean that the brain is particularly active just below the C1 electrode!

Because of volume conduction, we know only that something has happened somewhere in the brain (probably near C1), and this is being picked up at C1.

Many papers neglect volume conduction. They report on the specialization of some brain area, inferred from localized EEG signals.

However, without accounting for volume conduction, the location of the EEG signal may not relate one-to-one with the location of induced neuronal activity.

What can EEG researchers do?

While volume conduction introduces complexities, researchers can take steps to minimize its impact and enhance the accuracy of their findings.

  1. Choose recording configurations wisely. Depending on the research question, opt for bipolar or referential recording setups. We discussed this at length in our blog post on the number of EEG channels needed for research.
  2. Use advanced modeling techniques. Use advanced numerical modeling techniques to simulate the conductivity of different tissues and their impact on EEG signals (e.g., Ruiz-Gómez et al., 2019). For example, techniques like Boundary Element Method (BEM; Fuchs et al., 2001) and Finite Element Method (FEM; Awada et al., 1997) can provide more accurate representations of the head’s conductive properties.
  3. Consider new data analysis and signal processing techniques. Implement signal processing methods, like source localization, to separate genuine neural signals from volume conduction effects. For more, check out our blog post on removing noise in EEG.
  4. Incorporate multi-modal approaches. Combine EEG with other neuroimaging techniques like functional MRI (fMRI) or magnetoencephalography (MEG) to provide a more comprehensive understanding of brain activity.
  5. Use individualized approaches. Consider the variability in tissue conductivities among individuals. For instance, Tuch et al. (2001) used Diffusion Tensor MRI to create personalized conductivity models.


Understanding volume conduction is essential for accurate EEG data interpretation. The brain’s electrical signals do not travel in a straight line from source to electrode. They take a convoluted path influenced by the conductive properties of surrounding tissues.

Acknowledging and addressing the effects of volume conduction will lead to more precise insights into brain function. Through careful experimental design, advanced modeling, and thoughtful data analysis, we can mitigate the impact of volume conduction on our measurement of brain signals.

Interested to find out more? Feel free to contact us at


Awada, K. A., Jackson, D. R., Williams, J. T., Wilton, D. R., Baumann, S. B., & Papanicolaou, A. C. (1997). Computational aspects of finite element modeling in EEG source localization. In IEEE Transactions on Biomedical Engineering (Vol. 44, Issue 8, pp. 736–752). Institute of Electrical and Electronics Engineers (IEEE).

Beck, T. W., Housh, T. J., Cramer, J. T., Malek, M. H., Mielke, M., Hendrix, R., & Weir, J. P. (2007). A comparison of monopolar and bipolar recording techniques for examining the patterns of responses for electromyographic amplitude and mean power frequency versus isometric torque for the vastus lateralis muscle. In Journal of Neuroscience Methods (Vol. 166, Issue 2, pp. 159–167).

van den Broek, S. P., Reinders, F., Donderwinkel, M., & Peters, M. J. (1998). Volume conduction effects in EEG and MEG. In Electroencephalography and Clinical Neurophysiology (Vol. 106, Issue 6, pp. 522–534).

Fuchs, M., Wagner, M., & Kastner, J. (2001). Boundary element method volume conductor models for EEG source reconstruction. In Clinical Neurophysiology (Vol. 112, Issue 8, pp. 1400–1407).

Holsheimer, J., & Feenstra, B. W. A. (1977). Volume conduction and EEG measurements within the brain: A quantitative approach to the influence of electrical spread on the linear relationship of activity measured at different locations. In Electroencephalography and Clinical Neurophysiology (Vol. 43, Issue 1, pp. 52–58).

Ruiz-Gómez, S. J., Hornero, R., Poza, J., Maturana-Candelas, A., Pinto, N., & Gómez, C. (2019). Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Application to Alzheimer’s disease continuum. In Journal of Neural Engineering (Vol. 16, Issue 6, p. 066019).

Rutkove, S.B. (2007). Introduction to Volume Conduction. In: Blum, A.S., Rutkove, S.B. (eds) The Clinical Neurophysiology Primer. Humana Press.

Tuch, D. S., Wedeen, V. J., Dale, A. M., George, J. S., & Belliveau, J. W. (2001). Conductivity tensor mapping of the human brain using diffusion tensor MRI. In Proceedings of the National Academy of Sciences (Vol. 98, Issue 20, pp. 11697–11701).

Wolters, C., & Munck, J. (2007). Volume conduction. In Scholarpedia (Vol. 2, Issue 3, p. 1738).