Flexible 3D-Printed EEG Electrodes

Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors

EEG-enabled earbuds represent a promising frontier in brain activity monitoring beyond traditional laboratory testing. Their discrete form factor and proximity to the brain make them the ideal candidate for the first generation of discrete non-invasive brain-computer interfaces (BCIs). However, this new technology will require comprehensive characterization before we see widespread consumer and health-related usage. To address this need, we developed a validation toolkit that aims to facilitate and expand the assessment of ear-EEG devices. The first component of this toolkit is a desktop application ("EaR-P Lab") that controls several EEG validation paradigms. This application uses the Lab Streaming Layer (LSL) protocol, making it compatible with most current EEG systems. The second element of the toolkit introduces an adaptation of the phantom evaluation concept to the domain of ear-EEGs. Specifically, it utilizes 3D scans of the test subjects' ears to simulate typical EEG activity around and inside the ear, allowing for controlled assessment of different ear-EEG form factors and sensor configurations. Each of the EEG paradigms were validated using wet-electrode ear-EEG recordings and benchmarked against scalp-EEG measurements. The ear-EEG phantom was successful in acquiring performance metrics for hardware characterization, revealing differences in performance based on electrode location. This information was leveraged to optimize the electrode reference configuration, resulting in increased auditory steady-state response (ASSR) power. Through this work, an ear-EEG evaluation toolkit is made available with the intention to facilitate the systematic assessment of novel ear-EEG devices from hardware to neural signal acquisition.

The 3D Printing of Nanocomposites for Wearable Biosensors: Recent Advances, Challenges, and Prospects

Notably, 3D-printed flexible and wearable biosensors have immense potential to interact with the human body noninvasively for the real-time and continuous health monitoring of physiological parameters. This paper comprehensively reviews the progress in 3D-printed wearable biosensors. The review also explores the incorporation of nanocomposites in 3D printing for biosensors. A detailed analysis of various 3D printing processes for fabricating wearable biosensors is reported. Besides this, recent advances in various 3D-printed wearable biosensors platforms such as sweat sensors, glucose sensors, electrocardiography sensors, electroencephalography sensors, tactile sensors, wearable oximeters, tattoo sensors, and respiratory sensors are discussed. Furthermore, the challenges and prospects associated with 3D-printed wearable biosensors are presented. This review is an invaluable resource for engineers, researchers, and healthcare clinicians, providing insights into the advancements and capabilities of 3D printing in the wearable biosensor domain.

Advanced Electrode Technologies for Noninvasive Brain-Computer Interfaces

Brain-computer interfaces (BCIs) have garnered significant attention in recent years due to their potential applications in medical, assistive, and communication technologies. Building on this, noninvasive BCIs stand out as they provide a safe and user-friendly method for interacting with the human brain. In this work, we provide a comprehensive overview of the latest developments and advancements in material, design, and application of noninvasive BCIs electrode technology. We also explore the challenges and limitations currently faced by noninvasive BCI electrode technology and sketch out the technological roadmap from three dimensions: Materials and Design; Performances; Mode and Function. We aim to unite research efforts within the field of noninvasive BCI electrode technology, focusing on the consolidation of shared goals and fostering integrated development strategies among a diverse array of multidisciplinary researchers.

Flower electrodes for comfortable dry electroencephalography

Dry electroencephalography (EEG) electrodes provide rapid, gel-free, and easy EEG preparation, but with limited wearing comfort. We propose a novel dry electrode comprising multiple tilted pins in a flower-like arrangement. The novel Flower electrode increases wearing comfort and contact area while maintaining ease of use. In a study with 20 volunteers, we compare the performance of a novel 64-channel dry Flower electrode cap to a commercial dry Multipin electrode cap in sitting and supine positions. The wearing comfort of the Flower cap was rated as significantly improved both in sitting and supine positions. The channel reliability and average impedances of both electrode systems were comparable. Averaged VEP components showed no considerable differences in global field power amplitude and latency, as well as in signal-to-noise ratio and topography. No considerable differences were found in the power spectral density of the resting state EEGs between 1 and 40 Hz. Overall, our findings provide evidence for equivalent channel reliability and signal characteristics of the compared cap systems in the sitting and supine positions. The reliability, signal quality, and significantly improved wearing comfort of the Flower electrode allow new fields of applications for dry EEG in long-term monitoring, sensitive populations, and recording in supine position.

The Feature, Performance, and Prospect of Advanced Electrodes for Electroencephalogram

Recently, advanced electrodes have been developed, such as semi-dry, dry contact, dry non-contact, and microneedle array electrodes. They can overcome the issues of wet electrodes and maintain high signal quality. However, the variations in these electrodes are still unclear and not explained, and there is still confusion regarding the feasibility of electrodes for different application scenarios. In this review, the physical features and electroencephalogram (EEG) signal performances of these advanced EEG electrodes are introduced in view of the differences in contact between the skin and electrodes. Specifically, contact features, biofeatures, impedance, signal quality, and artifacts are discussed. The application scenarios and prospects of different types of EEG electrodes are also elucidated.

Real-time noise cancellation with deep learning

Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learning algorithm which produces adaptively a signal opposing the noise so that destructive interference occurs. As a proof of concept, we demonstrate the algorithm's performance by reducing electromyogram noise in electroencephalograms with the usage of a custom, flexible, 3D-printed, compound electrode. With this setup, an average of 4dB and a maximum of 10dB improvement of the signal-to-noise ratio of the EEG was achieved by removing wide band muscle noise. This concept has the potential to not only adaptively improve the signal-to-noise ratio of EEG but can be applied to a wide range of biological, industrial and consumer applications such as industrial sensing or noise cancelling headphones.

Application of 3D Printing Technology to Produce Hippocampal Customized Guide Cannulas

Implantation of guide cannulas is a widely used technique to access specific brain areas. Although commercially available, the need to personalize these implants and the high cost prompted us to design open-source customized devices taking advantage of 3D printing technology. Our cannulas consisted in a 3D-printed head mount designed according to the Paxinos coordinates to reach the CA1 area of the hippocampus. To cut guide cannulas to the proper length, we designed and realized an original 3D-printed linear motion apparatus. Polylactic acid thermoplastic polymer was used as printing material. Homemade or commercial cannulas were implanted in 4- to 6-month-old wild-type mice and intrahippocampal injections of amyloid-β peptide at different concentrations were performed. In vivo behavioral studies of novel object recognition indicated that results obtained with homemade versus commercial devices were comparable. Methylene blue injections and Nissl staining confirmed the correct localization of cannulas in the CA1 area of mouse hippocampus. Our method allows a fast manufacturing of hippocampal cannulas preserving the required precision at very low cost. Furthermore, this system can be easily modified to produce cannulas to target other brain areas. In conclusion, 3D printing might be used as a useful and versatile technology to realize open-source customized devices in neuroscience laboratories.

Fully printed and multifunctional graphene-based wearable e-textiles for personalized healthcare applications

Wearable e-textiles have gained huge tractions due to their potential for non-invasive health monitoring. However, manufacturing of multifunctional wearable e-textiles remains challenging, due to poor performance, comfortability, scalability, and cost. Here, we report a fully printed, highly conductive, flexible, and machine-washable e-textiles platform that stores energy and monitor physiological conditions including bio-signals. The approach includes highly scalable printing of graphene-based inks on a rough and flexible textile substrate, followed by a fine encapsulation to produce highly conductive machine-washable e-textiles platform. The produced e-textiles are extremely flexible, conformal, and can detect activities of various body parts. The printed in-plane supercapacitor provides an aerial capacitance of ∼3.2 mFcm⁻² (stability ∼10,000 cycles). We demonstrate such e-textiles to record brain activity (an electroencephalogram, EEG) and find comparable to conventional rigid electrodes. This could potentially lead to a multifunctional garment of graphene-based e-textiles that can act as flexible and wearable sensors powered by the energy stored in graphene-based textile supercapacitors.

•

Graphene-based screen-printed conductive, flexible, and machine-washable e-textiles

•

Able to capture movements demonstrating their potential as activity sensors

•

In-plane all-solid-state printed textiles supercapacitor showing comparable performance

•

Perform as EEG bio-signal electrode comparable to current rigid clinical electrodes

Multifactorial Effects of Gelling Conditions on Mechanical Properties of Skin-Like Gelatin Membranes Intended for In Vitro Experimentation and Artificial Skin Models

The development of new cosmetic products, skin contact medical devices, skin medicaments, wound care devices, tattooing and piercing has experienced an impressive growth in recent years. In parallel, new restrictions to in vivo experimentation in animals and humans have been widely implemented by regulatory authorities. New knowledge about alternative materials for in vitro skin-related experimentation is required to overcome these severe limitations. This paper presents a set of three 4-D surface response equations describing the mechanical properties of skin-like gelatin membranes intended for use as an alternative biomaterial for in vitro skin-related experimentation. The membranes were obtained by a sol-gel method. The novelty of this contribution is the establishment of the cross-dependency effects of key synthesis conditions on the final mechanical properties of gelatin membranes. The results of this work are useful to produce gelatin membranes with tailored mechanical properties mimicking different types of human skins. In particular, membranes with Young’s modulus of 1 MPa and maximum tensile strength of 0.85 MPa were obtained.

A Flexible Two-Sensor System for Temperature and Bending Angle Monitoring

A wearable electronic system constructed with multiple sensors with different functions to obtain multidimensional information is essential for making accurate assessments of a person’s condition, which is especially beneficial for applications in the areas of health monitoring, clinical diagnosis, and therapy. In this work, using polyimide films as substrates and Pt as the constituent material of serpentine structures, flexible temperature and angle sensors were designed that can be attached to the surface of an object or the human body for monitoring purposes. In these sensors, changes in temperature and bending angle are converted into variations in resistance through thermal resistance and strain effects with a sensitivity of 0.00204/°C for temperatures in the range of 25 to 100 °C and a sensitivity of 0.00015/° for bending angles in the range of 0° to 150°. With an appropriate layout design, two sensors were integrated to measure temperature and bending angles simultaneously in order to obtain decoupled, compensated, and more accurate information of temperature and angle. Finally, the system was tested by being attached to the surface of a knee joint, demonstrating its application potential in disease diagnosis, such as in arthritis assessment.

3D Printable Dry EEG Electrodes with Coiled-Spring Prongs

Various dry electroencephalography (EEG) electrodes have been developed. Dry EEG electrodes need to be pressed onto the scalp; therefore, there is a tradeoff between keeping the contact impedance low and maintaining comfort. We propose an approach to solve this tradeoff through the printing of complex-shaped electrodes by using a stereolithography 3D printer. To show the feasibility of our approach, we fabricated electrodes that have flexible fingers (prongs) with springs. Although dry electrodes with flexible prongs have been proposed, a suitable spring constant has not been obtained. In this study, the spring constant of our electrodes was determined from a contact model between the electrodes and the scalp. The mechanical properties and reproductivity of the electrodes were found to be sufficient. Finally, we measured the alpha waves when a participant opened/closed his eyes by using our electrodes.

Towards emerging EEG applications: a novel printable flexible Ag/AgCl dry electrode array for robust recording of EEG signals at forehead sites

OBJECTIVES

With the rapid development of EEG-based wearable healthcare devices and brain-computer interfaces, reliable and user-friendly EEG sensors for EEG recording, especially at forehead sites, are highly desirable and challenging. However, existing EEG sensors cannot meet the requirements, since wet electrodes require tedious setup and conductive pastes or gels, and most dry electrodes show unacceptable high contact impedance. In addition, the existing electrodes cannot absorb sweat effectively; sweat would cause cross-interferences, and even short circuits, between adjacent electrodes, especially in the moving scenarios, or a hot and humid environment. To resolve these problems, a novel printable flexible Ag/AgCl dry electrode array was developed for EEG acquisition at forehead sites, mainly consisting of screen printing the Ag/AgCl coating, conductive sweat-absorbable sponges and flexible tines.

APPROACH

A systematic method was also established to evaluate the flexible dry electrode array.

MAIN RESULTS

The experimental results show the flexible dry electrode array has reproducible electrode potential, relatively low electrode-skin impedance, and good stability. Moreover, the EEG signals can be effectively captured with a high quality that is comparable to that of wet electrodes.

SIGNIFICANCE

All the results confirmed the feasibility of forehead EEG recording in real-world scenarios using the proposed flexible dry electrode array, with a rapid and facile operation as well as the advantages of self-application, user-friendliness and wearer comfort.

3D Printed Neural Regeneration Devices

Neural regeneration devices interface with the nervous system and can provide flexibility in material choice, implantation without the need for additional surgeries, and the ability to serve as guides augmented with physical, biological (e.g., cellular), and biochemical functionalities. Given the complexity and challenges associated with neural regeneration, a 3D printing approach to the design and manufacturing of neural devices could provide next-generation opportunities for advanced neural regeneration via the production of anatomically accurate geometries, spatial distributions of cellular components, and incorporation of therapeutic biomolecules. A 3D printing-based approach offers compatibility with 3D scanning, computer modeling, choice of input material, and increasing control over hierarchical integration. Therefore, a 3D printed implantable platform could ultimately be used to prepare novel biomimetic scaffolds and model complex tissue architectures for clinical implants in order to treat neurological diseases and injuries. Further, the flexibility and specificity offered by 3D printed in vitro platforms have the potential to be a significant foundational breakthrough with broad research implications in cell signaling and drug screening for personalized healthcare. This progress report examines recent advances in 3D printing strategies for neural regeneration as well as insight into how these approaches can be improved in future studies.