Redefining brain activity monitoring with pioneering EEG textile technology

Redefining brain activity monitoring with pioneering EEG textile technology

13 Min.
By Bitbrain
November 13, 2023

In this article we will explore EEG technology based on sensors entirely made of smart textiles, based on the scientific paper "A garment that measures brain activity: proof of concept of an EEG sensor layer fully implemented with smart textiles" published by our Bitbrain scientific team: Eduardo López-Larraz, Carlos Escolano, Almudena Robledo, Leyre Morlas, Alexandra Alda and Javier Minguez. 

This research presents the advancement of EEG garments, which can measure brain activity with the same precision as advanced dry EEG systems. Notably, the distinctive characteristic is the EEG sensor layer, comprising electrodes and transmission systems that are made completely of threads, fabrics, and smart textiles, removing the necessity for metal or plastic. 

When tested on healthy participants, the EEG Garment demonstrated results similar to a state-of-the-art Dry-EEG system. However, it provided greater comfort and ergonomics, although slightly more susceptibility to suffering EEG artifacts. We provide the datasets recorded, offering the initial open-source dataset for a purely textile EEG sensor. These wearable EEGs have the potential to make neurotechnology more accessible and eco-friendly, potentially transforming the industry. 


The electroencephalogram (EEG) is a non-invasive technique used to measure brain activity. It is used for diagnosing conditions like epilepsy and ADHD, as well as for research and brain-computer interfaces. Efforts have been made to make EEGs wireless, smaller, and more affordable. The sensor layer, particularly the electrodes, is important for usability. Traditional wet electrodes require an electrolytic substance, but dry-EEG electrodes are more user-friendly. Researchers are exploring new conductive materials to improve dry electrodes.

Conductive textiles can be used to integrate EEG sensors into garments, providing comfort, cost-effectiveness, breathable, and washability. However, achieving a good signal-to-noise ratio is challenging. Few studies have advanced textile EEGs without non-textile components.

Infografia Blog Garment 01 (1)

Figure 1. Different technologies for electroencephalogram (EEG) devices with specific focus on the EEG sensor layer. (A) Example of “shower-cap” research-oriented EEG systems. (B) Example of “minimal” application-oriented dry EEG systems. (C) Proof-of-concept “garment” EEG system. For a complete review of similar devices from different manufacturers, see Niso et al. (2023). In all the EEG devices, we differentiate between the EEG sensor layer, the connector, and the amplifier. In panels (A,B), the sensor layer is implemented with plastics, metals, glues and materials from the electronic devices industries, while in panel (C), “garment” sensor layers are implemented with materials and manufacturing processes from the textile industry only.

This paper presents a groundbreaking EEG sensor layer manufactured exclusively from textile industry materials and processes. The EEG Garment system underwent a preliminary test on healthy participants and was compared to a standard Dry-EEG system. The system's performance was evaluated based on skin-electrode impedance, EEG activity, artifacts, and user comfort.

We created two headbands to collect EEG data from specific spots on the forehead for studying a new EEG sensor layer. We made a special headband that uses smart fabric to monitor brain activity. It has 4 recording electrodes, as well as a reference and ground. There is a connector at the back where the amplifier is connected. The amplifier samples the signals at 256 Hz and sends the data to a laptop using Bluetooth. We also made another headband with the same setup, but it uses regular electrodes and cables.

Materials and methods

We designed an EEG sensor layer that only uses smart textiles to monitor the brain activity from the forehead (see Figure 1A-B). It includes 4 recording electrodes, plus reference and ground, and a connector at the back part, where the amplifier is attached. The amplifier samples the signals at 256 Hz and uses Bluetooth Low Energy to send the data to a laptop. For comparison, we designed a second headband with the same configuration, but using standard dry Ag/AgCl electrodes (Dry-EEG) and coaxial cables (see Figure 1C).

EEG garment headband

The EEG Garment headband is a pioneering wearable technology made entirely from textile materials. It features electrodes (Textrodes) crafted from 3-strand silver-coated Nylon conductive yarns with 114 Ω/m linear resistance and a unique embroidery pattern for precise data capture. The electrodes are distributed over different areas, with Fp1 and Fp2 electrodes covering 2.8 cm2, and F7 and F8 electrodes covering 7.4 cm2, accommodating various head sizes. The signal transmission system emulates a coaxial cable, using layered textiles and Ultraflex Tape Zell RS conductive fabric for effective signal shielding. The Textrodes connect to the transmission core via embroidery and to the connector through a rigid PCB stitched with conductive wire. The headband's support structure is made from satin-structured polyester fabric, weighing 120 g/m2, ensuring comfort and durability.

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Figure 2. EEG Garment and Dry-EEG headbands. (A) Detail of EEG garment electrode shape and location. (B) EEG Garment headband, worn by one participant. (C) Detail of Dry-EEG electrode shape and location. (D) Dry-EEG headband worn by one participant. 

The primary objective of this research was to provide initial human validation of the technology. Our design decisions, which focused on sub-hairline EEG, were implemented to simplify the setup, ensure reproducibility, and prioritize the quality of electrophysiological data while limiting potential artifacts. 

Experimental validation

We carried out two experimental studies. In the first one, we characterized the impedance of the electrodes and signal transmission of the new EEG garment system, while in the second, we quantified common spontaneous (frequency domain) and evoked (time-frequency domain) EEG patterns of activity.  Six healthy volunteers participated in the first study (5 females, and 1 male, age: 30.2 ± 5.8 years) and ten different healthy volunteers participated in the second study (5 females, 5 males, age: 27.8 ± 3.7 years). 

Study 1: Impedance analysis

Impedances for the EEG Garment and Dry-EEG sensor layers were evaluated. For a baseline, a Wet-EEG setup, involving the Dry-EEG headband with an electrolytic gel after skin cleaning with an abrasive gel, was used. 
Normally, skin prep with abrasive gel is standard for EEG. However, for EEG Garment and Dry-EEG, only make-up remover wipes cleaned the skin.

Study 2: EEG activity analysis 

Participants' brain activity was monitored using both headbands. Tasks were performed with both technologies, with participants starting alternately with EEG Garment or Dry-EEG. Tasks included: 

  • Task 1: Resting with eyes closed for 3 minutes
  • Task 2: Resting with eyes open for 3 minutes.
  • Task 3: The experiment involved performing 80 right-arm reaching movements, guided by visual cues within presentation blocks.
  • Task 4: EEG artifacts were induced through actions such as blinking, tongue, and jaw movements, among others.

EEG analysis

Frequency analysis

The power spectral density of the resting states during eyes-closed, and eyes-open conditions, as well as during artifact induction recordings, was evaluated with a specific focus. 

  • Preprocessing: Outlier amplitude values were removed from EEG recordings by high-pass filtering at 60 Hz, applying a Hilbert transform, and z-scoring. Any sample above 5 standard deviations from the mean was discarded, and gaps were filled via linear interpolation. Artifact induction task signals were exempt.
  • Processing: EEG signals were filtered between 0.1 and 100 Hz. Power distribution between 0 and 60 Hz was estimated using FFT. The logarithm of power values was computed.
  • Statistics: Power values from the four electrodes were averaged. Mean power for each participant and headband was calculated across five frequency bands (delta, theta, alpha, beta, and electrical noise). A paired Wilcoxon signed-rank test assessed differences between headbands per frequency band. 

Time-frequency analysis 

  • Preprocessing: Outlier amplitude values were removed from EEG recordings by high-pass filtering at 60 Hz, applying a Hilbert transform, and z-scoring. Any sample above 5 standard deviations from the mean was discarded, and gaps were filled via linear interpolation. Artifact induction task signals were exempt. 
  • Processing: EEG signals were filtered between 0.1 and 100 Hz. Power distribution between 0 and 60 Hz was estimated using FFT. The logarithm of power values was computed. 
  • Statistics: Power values from the four electrodes were averaged. Mean power for each participant and headband was calculated across five frequency bands (delta, theta, alpha, beta, and electrical noise). A paired Wilcoxon signed-rank test assessed differences between headbands per frequency band. 

Comfort and perception assessment

Post EEG recording, participants answered a questionnaire regarding: 

  • Overall system comfort, rated 1 (least) to 7 (most). 
  • EEG electrode comfort, rated 1 (least) to 7 (most). 
  • Weight perception of the system, rated from 1 (unnoticeable) to 4 (very noticeable and bothersome). 
  • Stability perception of the system, rated from 1 (stable) to 3 (unstable). 


Impedance analysis

The study presented impedance values for EEG Garment, Dry-EEG, and Wet-EEG (Dry-EEG with gel). EEG Garments' impedance was notably higher than Dry-EEG, both being significantly above Wet-EEG. The findings align with established values for both wet and dry EEG systems as reported by Lopez-Gordo et al., 2014 and Shad et al., 2020. (Figure 3) 

Impedance analysis

Figure 3. Impedance results for EEG Garment, Dry-EEG, and Wet-EEG. Impedance is displayed as a function of the frequency. The values for all the subjects, electrodes and repeated measurements for each electrode are averaged for each technology. The shades represent the standard error of the mean. Notice that the y-axis is displayed on a logarithmic scale. The dashed horizontal line displays the value of 5 KΩ, as a standard threshold for good impedance in wet EEG systems (Nuwer et al., 1998).

Frequency analysis of EEG during the resting state/artifact induction 

EEG Garment and Dry-EEG provided comparable EEG signals during the resting state, with strong alpha waves during eyes-closed which were suppressed during eyes-open. Power spectral density analysis revealed equivalent values for the two systems at common EEG study frequencies (below 30 Hz) (Figure 4). Statistical comparisons showed no significant power differences in the delta, theta, alpha, and beta frequencies between the two systems during the resting state. 

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Figure 4. Frequency analysis. Statistical comparisons between both headbands on five frequency bands during (A) eyes-closed, (B) eyes-open, and (C) artifact induction. n.s., non-significant; **p < 0.01.

The research compared two EEG systems, one with electrodes embedded in a garment (EEG Garment) and the other with dry electrodes (Dry-EEG). The research found that both systems produced similar signals in morphology and amplitude during resting states. Strong alpha waves were also observed when subjects had their eyes closed.

Both systems produced similar signals in terms of morphology and amplitude during resting states, with strong alpha waves visible when subjects had their eyes closed, which were suppressed upon opening the eyes, at which time blinks became noticeable. Power spectral density analysis showed that the EEG signal values in frequencies typically analyzed (below 30 Hz) were equivalent between both systems during rest, with a clear alpha peak around 10 Hz in the frontopolar and frontal locations.

However, the EEG Garment was more susceptible to artifacts, exhibiting higher power at 50 Hz across all channels, and higher broadband power during the artifact induction task. During resting states, there were no significant differences in EEG power across the delta, theta, alpha, and beta frequency bands between the two systems. During artifact induction, the power in these frequencies was significantly higher for the EEG Garment compared to the Dry-EEG. Additionally, in the 45-55 Hz frequency band, the EEG Garment recorded significantly higher power both during resting states and during artifact induction. 

Time-frequency analysis of EEG during movement execution

Both EEG Garment and Dry-EEG headbands captured ERD/ERS maps during reaching movements. However, the Dry EEG headband produced a stronger ERD, particularly between 5 and 15 Hz during movement execution. (Figure 5) This analysis showed that the Dry EEG headband provided a significantly stronger ERD between 5 and 15 Hz during the execution of the reaching movements. (Figure 5)

Infografia Blog Garment

Figure 5. Time-frequency analysis. Comparison between the two technologies; (left) average of all the EEG Garment electrodes, (right) average of all the Dry-EEG electrodes.

Comfort metrics

On average, participants spent 42-48 minutes with each headband. According to their feedback, they found the EEG Garment to be highly comfortable, while the Dry-EEG was considered moderately to highly comfortable. In terms of weight perception, both systems were either not noticeable or slightly noticeable without causing any discomfort or inconvenience. Participants reported predominantly positive stability perception regarding both systems.

Comfort Garment


Accessible electroencephalographic (EEG) systems are a first step towards the democratisation of neurotechnology. Wearable market indicators predict the widespread adoption of wearable neurotechnology within one or two decades, similar to the adoption of other wearables a few years ago (Johnson and Picard, 2020). Innovations in dry EEG hardware have expanded the range of tools available to monitor brain activity. However, although there has been progress in the last decade, it is still premature to consider that dry electrodes have completely transformed the way EEG is utilised.

The main barriers to the widespread adoption of EEG systems in neurotechnology and non-invasive brain-computer interfaces are user acceptance based on ergonomics, usability and price. In theory, neurotechnology that is naturally accepted in everyday life can be enabled by EEG sensor layers integrated into textiles and garments.

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 In addition to this, EEG implementation in the textile industry would lead to lower manufacturing costs and much less pollution when compared to the metal and plastic industries. 

This research proposes and quantitatively characterises a proof of concept for an EEG-measuring garment; a system that uses only textile materials for its sensor layer. The development and refinement of this technology could facilitate its implementation beyond scientific laboratories or clinics, for the purposes of real-world research, home-based wellness applications, or patient diagnostics, treatment, and follow-up.

The study comparing EEG Garment to traditional metal-based dry EEG systems reveals significant findings concerning signal quality and susceptibility to artifacts. In scenarios of rest, both systems presented corresponding EEG recordings, with no marked power discrepancies for frequencies below 30 Hz, which includes the crucial alpha wave activity.

 The potential of textile-based EEG systems to collect large amounts of data and use advanced machine learning techniques for analysis is promising and warrants further development and optimisation.

Applications of garments to measure EEG

The integration of smart textiles in the manufacture of EEG devices offers a promising avenue for neurotechnology, combining user acceptance and cost-effectiveness. 

Applications for  EEG garment-based systems span the medical, research and wellness/leisure sectors. In the medical field, near-term applications include ambulatory brain monitoring for patient follow-up, seizure detection and sleep assessment. This technology could be particularly useful in sleep studies for preliminary assessments, long-term monitoring of epilepsy patients, or brain monitoring in neonates and intensive care patients. Sub-hairline and low-density EEG montages, already part of clinical practice, can use these textile systems for emergency and rapid triage scenarios.

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Looking further ahead, EEG garment-based, combined with advanced AI techniques, holds promise for tracking biomarkers in neurological disorders like cognitive decline, Parkinson’s disease, stroke, or spinal cord injuries. These systems could also support home-based therapies, such as motor rehabilitation, cognitive enhancement, or closed-loop neurostimulation.

In research, EEG garment devices can revolutionize out-of-the-lab brain monitoring, enabling large-scale, ecological studies. This technology could facilitate brain activity measurement in daily life scenarios and support studies with significantly larger participant pools.

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Outside clinical and research domains, EEG monitoring garments have potential applications in education, wellness, sports, and industrial settings. They could join the suite of biosignal monitoring devices like smartwatches, rings, or chest bands, expanding the range of measurable bio-signals and enhancing user engagement in diverse environments.

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This paper presented the first proof of concept of a garment to measure EEG activity. We compared the EEG Garment with a state-of-the-art Ag/AgCl dry EEG system in terms of spontaneous and evoked EEG activity, artifacts, skin-electrode impedance, and comfort. Under favourable recording conditions (low level of artifacts), the EEG Garment provided comparable measurements to the metal-based EEG system, although it is more prone to artifacts in adverse conditions, due to poorer contact impedances. 

One of the most relevant advantages of this innovation is that it opens the door to creating EEG technology that can be broadly adopted by the general population, due to its comfort and its reduced manufacturing cost. This could allow large scale recordings for clinical and non-clinical purposes in ecological conditions. 

To promote open science and replicability, we are providing public access to our datasets, thus releasing the first open-access dataset of an EEG sensor layer built exclusively with textiles and allowing its comparison with a metal-based dry EEG.  For more information you can access the full paper “A garment that measures brain activity: proof of concept of an EEG sensor layer fully implemented with smart textiles” 

An important final remark about the opportunities that this technology could enable is that this paradigm shift in brain monitoring should not take place without paying sufficient attention to its inherent risks. Experts in neuroethics are already working on creating recommendations for researchers, manufacturers and regulators to facilitate a responsible development of the neurotech field and its safe integration into our daily lives (Yuste et al., 2017).  

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Our research and development of textile-based EEG technologies have led to the creation of this innovative product. Discover the Ikon EEG, our new wearable textile EEG designed for real-world neuroscience research, anytime, anywhere by anyone.

For more details or to get in touch with our team, please contact us here: Ikon


Lopez-Gordo, M. A., Sanchez Morillo, D., and Pelayo Valle, F. (2014). Dry EEG electrodes. Sensors 14, 12847–12870. doi: 10.3390/s140712847 PubMed Abstract

Johnson, K. T., and Picard, R. W. (2020). Advancing neuroscience through wearable devices. Neuron 108, 8–12. doi: 10.1016/J.NEURON.2020.09.030 PubMed Abstract

Nuwer, M. R., Comi, G., Emerson, R., Fuglsang-Frederiksen, A., Guérit, J.-M., Hinrichs, H., et al. (1998). IFCN Standards IFCN standards for digital recording of clinical EEG. Electroencephalogr. Clin. Neurophysiol. 106, 259–261. Google Scholar

Yuste, R., Goering, S., Arcas, B. A. Y., Bi, G., Carmena, J. M., Carter, A., et al. (2017). Four ethical priorities for neurotechnologies and AI. Nature 551, 159–163. doi: 10.1038/551159a PubMed Abstract

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