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The Wet-EEG Cap & Differences Between Semi-Dry, Saline or Gel EEG caps
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A Wet-EEG cap is a type of electroencephalogram (EEG) device that requires the use of an electrolytic substance to improve scalp-electrode conductivity. This term is used in opposition to dry-EEG. This post will help you understand the differences between those concepts and focus on the key points to be aware of when selecting a wet-EEG system.

What is a wet-EEG cap? 

The main difference between EEG systems is the type of electrolytic substance used to improve the conductivity between the surface electrodes and the scalp (Liao 2012), part of the EEG sensory layer or EEG cap (more info here):

  1. Dry electrodes EEG systems: no substance. Get more info about dry EEG systems here.
  2. Wet EEG caps/systems:
    1. Gel EEG: electrolytic gels.
    2. Saline EEG: saline solutions.
    3. Semi-dry EEG or Water-based EEG: tap water humidity / adaptable disposal.

Gel, semi-dry, and saline EEG systems are usually referred to as wet-EEG. The fact that they require an electrolytic substance to improve the conductivity between the sensor and the scalp has a very important impact on the headset usability and comfort, and thus, on the final user experience and possible applications. 

Gel and Saline EEG electrodes

Gel and saline electrodes have been the most frequently used sensors for measuring EEG signals in the recent past. They require the application of a highly conductive electrolytic substance (electro gel or EEG gel) between the scalp and the electrode to obtain good contact and reduce the impedance of the skin-electrode interface. 

Sometimes before using these electrodes, the skin is degreased and abraded to improve the contact quality. After that, an EEG cap with electrodes is placed on the head or the electrodes are pasted with collodion to the scalp. Gel is then applied with a syringe, usually via a hole in the electrode.  The main features of the solution are components and viscosity.

  1. Components of the solution (Saline or Non-saline): The saline solution in the gel improves the conductivity, reducing the skin-electrode impedance, but can cause discomfort if the skin has been abraded before gel application. 
  2. Viscosity:
    • Liquid: These substances reach the skin easily, but they can spread and create unwanted bridges between electrodes.  
    • Gel: Easy application with a syringe. Gel viscosity allows it to be used in more dense electrode configurations, but the gel dries over time and it is not recommended for long recordings (i.e. more than 3-4 hours).
    • Paste: Difficult to apply and remove. Useful for long recordings or protocols that require movement.

The advantage of wet electrodes is that they have a better signal to noise ratio than dry electrodes, and therefore require simpler layers in the amplifier devices (easy to design and less expensive).

Their disadvantages are:

  1. They are less comfortable to wear.
  2. They need a substance to be placed between the electrode and the skin which requires some hair cleaning and stronger hygienic procedures for the equipment.
  3. Abrasion of the skin is sometimes required, which is an uncomfortable process for the participant.
  4. They need instruments like syringes to place the substance between the electrode and the skin, increasing the maintenance cost.

Semi-dry or Water-based EEG electrodes

Semi-dry EEG systems (Dey et al, 2019; Hua et al. 2019; Wang et al., 2016; Nijholt, 2019), also known as water-based EEG caps, are a novel subgroup of wet-EEG headsets that aim to overcome the drawbacks of current wet and dry electrodes by using tap water within an absorbent material to fill the gap between the sensor and the skin. 

Some semi-dry electrodes release a small amount of saline solution with the assistance of capillary force through porous ceramic pillars, eliminating the need of skin preparation and gel application (Hua et al. 2019). 

Others use a similar concept to create humidity by using highly absorbable porous sponges that are dampened with regular tap water. In both cases, the advantage is a quick setup, self-application, up to an 8-hour usage window, and cleanliness for the user (Dey et al, 2019). 

3. Dry vs Wet EEG systems in application

Dry-EEG electrodes do not require the use of any substance (see more info here about dry-EEG headsets) and they contact the scalp directly. Dry-EEG advantages are that they are set up quickly, they are usually comfortable to wear, do not require any additional instruments like syringes or gel cans, do not require cleaning of the head or hair after use, and do not require heavy hygienic procedures on the equipment afterward. Their main disadvantage is the high contact impedance between the sensor and the skin, which reduces the signal to noise ratio. Therefore the sensor and amplifier layers need to be able to deal with higher noise and signal artifacts (see Li et al., 2018). 

wet eeg cap vs. dry eeg electrodes

Wet EEG electrodes, on the other hand, can be used to record EEG signals in most situations, but they are more research-oriented than dry electrodes. They usually have an electrode system with a higher number of electrode locations, higher head coverage, and better signal quality. The main purpose of dry technology is to address real-world environments or professional products or services while the semidry-EEG cap is developed for more exploratory research settings. 

An easy way to fit both wet and dry-based electrodes EEG in a general research pipeline is:

  • Phase 1 - Exploratory research phase: use in-lab research technologies (wet-EEG caps) to understand human behavior in controlled situations. At this step, the priorities are to obtain measurements with a large number of sensors, with high coverture of the brain, and with very high resolution and accuracy. 
  • Phase 2 - Optimization of the application: use EEG data signal processing techniques to understand where and how the neural correlates underlying the behavior can be measured.
  • Phase 3 - Application-oriented phase: use out-of-lab research and application technologies (dry-EEG headsets) to understand human behavior in natural scenarios. In this case, the priority is to have easy to use and comfortable EEG technologies, with electrode positioning only over the relevant brain areas (measuring only the brain activities we need to measure), with mobility and resistance to artifacts (to handle free of movements).

So, the main application focus of wet-based EEG is usually complementary to dry-EEG, as they are used in different stages of this research pipeline. See some examples below brain-computer interface systems (BCI based applications).

phases to select a dry eeg headset based on the application

Important features with the wet-EEG cap

In the table below you can find a summary of the key aspects to look for in a semidry-EEG cap, not only about the electrode layer but also about the cables and the amplifier (Li et al., 2018; Wang et al., 2016; Tallgren et al., 2005). We next describe the main sensor features with the impact that they have and the recommended value for each feature. 

Features of wet EEG cap

Real-world Examples of Semi-dry EEG systems in use

1. CORBYS - Brain-computer interface scientific projects. 

The European project CORBYS (2011-2015) had the objective of neurorehabilitation of inferior limbs by recovering mobility in patients who had suffered a stroke or brain injury with an EEG-based brain-computer interface (BCI). The basic principle that the technology controls, consisted of making the movement of the lower body only when the patient had the motor cortex activated sufficiently and that a total adhesion to movement was being detected. In addition, the technology carried out the mobilization in a completely adaptive way. The CORBYS project was funded by the European Commission’s FP7, led by the University of Bremen (Germany), and organizations such as Imperial College (UK), Otto Block Mobility and Bitbrain (Spain) participated in it.

2. Moregrasp Project - Brain-computer interface (BCI) research for spinal cord injury using Bitbrain EEG. 

Patients with spinal cord injury (SCI) can suffer permanent paralysis, impeding them from performing simple everyday actions like grabbing a glass. BCIs can be used to detect when the patient is trying to perform a movement (e.g., by decoding movement attempts or motor imagery) and then to provide electrical stimulation to the muscles in order to facilitate the intended action. This type of neuroprostheses offers a natural way of recovering lost function by bypassing the injury and allowing the patient to use their own limb.

MoreGrasp was an EU H2020 project that aimed at developing noninvasive BCIs and neuroprostheses to support grasp function in patients with SCI. During the MoreGrasp project, Bitbrain developed two different EEG recording systems specially designed to be integrated with motor neuroprostheses. 

References

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  • Antelis, J. M., Montesano, L., Ramos-Murguialday, A., Birbaumer, N., & Minguez, J. (2012, August). Continuous decoding of intention to move from contralesional hemisphere brain oscillations in severely affected chronic stroke patients. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 4099-4103). IEEE.
  • Dey, N., Ashour, A. S., Fong, S. J., & Bhatt, C. (Eds.). (2019). Wearable and Implantable Medical Devices: Applications and Challenges. Academic Press.
  • Hua, H., Tang, W., Xu, X., Feng, D. D., & Shu, L. (2019). Flexible multi-layer semi-dry electrode for scalp EEG measurements at Hairy sites. Micromachines, 10(8), 518.
  • López-Larraz, E., Montesano, L., Gil-Agudo, Á., & Minguez, J. (2014). Continuous decoding of movement intention of upper limb self-initiated analytic movements from pre-movement EEG correlates. Journal of neuroengineering and rehabilitation, 11(1), 153.
  • Guixeres, J., Bigné, E., Ausín Azofra, J. M., Alcañiz Raya, M., Colomer Granero, A., Fuentes Hurtado, F., & Naranjo Ornedo, V. (2017). Consumer neuroscience-based metrics predict recall, liking and viewing rates in online advertising. Frontiers in psychology, 8, 1808.
  • Li, G., Wang, S., & Duan, Y. Y. (2018). Towards conductive-gel-free electrodes: Understanding the wet electrode, semi-dry electrode and dry electrode-skin interface impedance using electrochemical impedance spectroscopy fitting. Sensors and Actuators B: Chemical, 277, 250–260. DOI: 10.1016/j.snb.2018.08.155 
  • Liao, Lun-De & Lin, Chin-Teng & McDowell, Kaleb & Wickenden, Alma & Gramann, Klaus & Jung, Tzyy-Ping & Ko, Li-Wei & Chang, Jyh-Yeong. (2012). Biosensor Technologies for Augmented Brain-Computer Interfaces in the Next Decades. Proceedings of the IEEE. 100. 10.1109/JPROC.2012.2184829. 
  • Nijholt, A. (Ed.). (2019). Brain Art: Brain-Computer Interfaces for Artistic Expression. Springer.
  • Pinegger, A., Wriessnegger, S. C., Faller, J., & Müller-Putz, G. R. (2016). Evaluation of Different EEG Acquisition Systems Concerning Their Suitability for Building a Brain-Computer Interface: Case Studies. Frontiers in Neuroscience, 10. DOI: 10.3389/fnins.2016.00441 
  • Tallgren, P., Vanhatalo, S., Kaila, K., & Voipio, J. (2005). Evaluation of commercially available electrodes and gels for the recording of slow EEG  potentials. Clinical Neurophysiology, 116(4), 799–806. DOI: 10.1016/j.clinph.2004.10.001 
  • Wang, F., Li, G., Chen, J., Duan, Y., & Zhang, D. (2016). Novel semi-dry electrodes for brain-computer interface applications. Journal of neural engineering, 13(4), 046021.

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