A novel hydrogel electrolyte extender for rapid application of EEG sensors and extended recordings

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.

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

A Pre-gelled EEG Electrode and Its Application in SSVEP-based BCI

Electroencephalogram (EEG) electrodes are critical devices for brain-computer interface and neurofeedback. A pre-gelled (PreG) electrode was developed in this paper for EEG signal acquisition with a short installation time and good comfort. A hydrogel probe was placed in advance on the Ag/AgCl electrode before wearing the EEG headband instead of a time-consuming gel injection after wearing the headband. The impedance characteristics were compared between the PreG electrode and the wet electrode. The PreG electrode and the wet electrode performed the Brain-Computer Interface (BCI) application experiment to evaluate their performance. The average impedance of the PreG electrode can be decreased to 43 kΩ or even lower, which is higher than the wet electrode with an impedance of 8 kΩ. However, there is no significant difference in classification accuracy and information transmission rate (ITR) between the PreG electrode and the wet electrode in a 40 target BCI system based on Steady State Visually Evoked Potential (SSVEP). This study validated the efficiency of the proposed PreG electrode in the SSVEP-based BCI. The proposed PreG electrode will be an excellent substitute for wet electrodes in an actual application with convenience and good comfort.

Me-Doped Ti-Me Intermetallic Thin Films Used for Dry Biopotential Electrodes: A Comparative Case Study

In a new era for digital health, dry electrodes for biopotential measurement enable the monitoring of essential vital functions outside of specialized healthcare centers. In this paper, a new type of nanostructured titanium-based thin film is proposed, revealing improved biopotential sensing performance and overcoming several of the limitations of conventional gel-based electrodes such as reusability, durability, biocompatibility, and comfort. The thin films were deposited on stainless steel (SS) discs and polyurethane (PU) substrates to be used as dry electrodes, for non-invasive monitoring of body surface biopotentials. Four different Ti–Me (Me = Al, Cu, Ag, or Au) metallic binary systems were prepared by magnetron sputtering. The morphology of the resulting Ti–Me systems was found to be dependent on the chemical composition of the films, specifically on the type and amount of Me. The existence of crystalline intermetallic phases or glassy amorphous structures also revealed a strong influence on the morphological features developed by the different systems. The electrodes were tested in an in-vivo study on 20 volunteers during sports activity, allowing study of the application-specific characteristics of the dry electrodes, based on Ti–Me intermetallic thin films, and evaluation of the impact of the electrode–skin impedance on biopotential sensing. The electrode–skin impedance results support the reusability and the high degree of reliability of the Ti–Me dry electrodes. The Ti–Al films revealed the least performance as biopotential electrodes, while the Ti–Au system provided excellent results very close to the Ag/AgCl reference electrodes.

A high-density 256-channel cap for dry electroencephalography

High‐density electroencephalography (HD‐EEG) is currently limited to laboratory environments since state‐of‐the‐art electrode caps require skilled staff and extensive preparation. We propose and evaluate a 256‐channel cap with dry multipin electrodes for HD‐EEG. We describe the designs of the dry electrodes made from polyurethane and coated with Ag/AgCl. We compare in a study with 30 volunteers the novel dry HD‐EEG cap to a conventional gel‐based cap for electrode‐skin impedances, resting state EEG, and visual evoked potentials (VEP). We perform wearing tests with eight electrodes mimicking cap applications on real human and artificial skin. Average impedances below 900 kΩ for 252 out of 256 dry electrodes enables recording with state‐of‐the‐art EEG amplifiers. For the dry EEG cap, we obtained a channel reliability of 84% and a reduction of the preparation time of 69%. After exclusion of an average of 16% (dry) and 3% (gel‐based) bad channels, resting state EEG, alpha activity, and pattern reversal VEP can be recorded with less than 5% significant differences in all compared signal characteristics metrics. Volunteers reported wearing comfort of 3.6 ± 1.5 and 4.0 ± 1.8 for the dry and 2.5 ± 1.0 and 3.0 ± 1.1 for the gel‐based cap prior and after the EEG recordings, respectively (scale 1–10). Wearing tests indicated that up to 3,200 applications are possible for the dry electrodes. The 256‐channel HD‐EEG dry electrode cap overcomes the principal limitations of HD‐EEG regarding preparation complexity and allows rapid application by not medically trained persons, enabling new use cases for HD‐EEG.

The Arch Electrode: A Novel Dry Electrode Concept for Improved Wearing Comfort

Electroencephalography (EEG) is increasingly used for repetitive and prolonged applications like neurofeedback, brain computer interfacing, and long-term intermittent monitoring. Dry-contact electrodes enable rapid self-application. A common drawback of existing dry electrodes is the limited wearing comfort during prolonged application. We propose a novel dry Arch electrode. Five semi-circular arches are arranged parallelly on a common baseplate. The electrode substrate material is a flexible thermoplastic polyurethane (TPU) produced by additive manufacturing. A chemical coating of Silver/Silver-Chloride (Ag/AgCl) is applied by electroless plating using a novel surface functionalization method. Arch electrodes were manufactured and validated in terms of mechanical durability, electrochemical stability, in vivo applicability, and signal characteristics. We compare the results of the dry arch electrodes with dry pin-shaped and conventional gel-based electrodes. 21-channel EEG recordings were acquired on 10 male and 5 female volunteers. The tests included resting state EEG, alpha activity, and a visual evoked potential. Wearing comfort was rated by the subjects directly after application, as well as at 30 min and 60 min of wearing. Our results show that the novel plating technique provides a well-adhering electrically conductive and electrochemically stable coating, withstanding repetitive strain and bending tests. The signal quality of the Arch electrodes is comparable to pin-shaped dry electrodes. The average channel reliability of the Arch electrode setup was 91.9 ± 9.5%. No considerable differences in signal characteristics have been observed for the gel-based, dry pin-shaped, and arch-shaped electrodes after the identification and exclusion of bad channels. The comfort was improved in comparison to pin-shaped electrodes and enabled applications of over 60 min duration. Arch electrodes required individual adaptation of the electrodes to the orientation and hairstyle of the volunteers. This initial preparation time of the 21-channel cap increased from an average of 5 min for pin-like electrodes to 15 min for Arch electrodes and 22 min for gel-based electrodes. However, when re-applying the arch electrode cap on the same volunteer, preparation times of pin-shaped and arch-shaped electrodes were comparable. In summary, our results indicate the applicability of the novel Arch electrode and coating for EEG acquisition. The novel electrode enables increased comfort for prolonged dry-contact measurement.

Flexible graphene/GO electrode for gel-free EEG

Objective.Developments in electroencephalography (EEG) technology have allowed the use of the brain-computer interface (BCI) outside dedicated labratories. In order to achieve long-term monitoring and detection of EEG signals for BCI application, dry electrodes with good signal quality and high bio compatibility are essential. In 2016, we proposed a flexible dry electrode made of silicone gel and Ag flakes, which showed good signal quality and mechanical robustness. However, the Ag components used in our previous design made the electrode too expensive for commercial adaptation.Approach.In this study, we developed an affordable dry electrode made of silicone gel, metal flakes and graphene/GO based on our previous design. Two types of electrodes with different graphene/GO proportions were produced to explore how the amount of graphene/GO affects the electrode.Main results.During our tests, the electrodes showed low impedance and had good signal correlation to conventional wet electrodes in both the time and frequency domains. The graphene/GO electrode also showed good signal quality in eyes-open EEG recording. We also found that the electrode with more graphene/GO had an uneven surface and worse signal quality. This suggests that adding too much graphene/GO may reduce the electrods' performance. Furthermore, we tested the proposed dry electrodes' capability in detecting steady state visually evoked potential. We found that the dry electrodes can reliably detect evoked potential changes even in the hairy occipital area.Significance.Our results showed that the proposed electrode has good signal quality and is ready for BCI applications.

Review of semi-dry electrodes for EEG recording

Developing reliable and user-friendly electroencephalography (EEG) electrodes remains a challenge for emerging real-world EEG applications. Classic wet electrodes are the gold standard for recording EEG; however, they are difficult to implement and make users uncomfortable, thus severely restricting their widespread application in real-life scenarios. An alternative is dry electrodes, which do not require conductive gels or skin preparation. Despite their quick setup and improved user-friendliness, dry electrodes still have some inherent problems (invasive, relatively poor signal quality, or sensitivity to motion artifacts), which limit their practical utilization. In recent years, semi-dry electrodes, which require only a small amount of electrolyte fluid, have been successfully developed, combining the advantages of both wet and dry electrodes while addressing their respective drawbacks. Semi-dry electrodes can collect reliable EEG signals comparable to wet electrodes. Moreover, their setup is as fast and convenient similar to that of dry electrodes. Hence, semi-dry electrodes have shown tremendous application prospects for real-world EEG acquisition. Herein, we systematically summarize the development, evaluation methods, and practical design considerations of semi-dry electrodes. Some feasible suggestions and new ideas for the development of semi-dry electrodes have been presented. This review provides valuable technical support for the development of semi-dry electrodes toward emerging practical applications.

MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG

Reference electrode standardization technique (REST) has been increasingly acknowledged and applied as a re-reference technique to transform an actual multi-channels recordings to approximately zero reference ones in electroencephalography/event-related potentials (EEG/ERPs) community around the world in recent years. However, a more easy-to-use toolbox for re-referencing scalp EEG data to zero reference is still lacking. Here, we have therefore developed two open-source MATLAB toolboxes for REST of scalp EEG. One version of REST is closely integrated into EEGLAB, which is a popular MATLAB toolbox for processing the EEG data; and another is a batch version to make it more convenient and efficient for experienced users. Both of them are designed to provide an easy-to-use for novice researchers and flexibility for experienced researchers. All versions of the REST toolboxes can be freely downloaded at http://www.neuro.uestc.edu.cn/rest/Down.html, and the detailed information including publications, comments and documents on REST can also be found from this website. An example of usage is given with comparative results of REST and average reference. We hope these user-friendly REST toolboxes could make the relatively novel technique of REST easier to study, especially for applications in various EEG studies.

Understanding the Influences of EEG Reference: A Large-Scale Brain Network Perspective

The influence of reference is a critical issue for the electroencephalography (EEG) and event-related potentials (ERPs) studies. However, previous investigations concentrated less on the location of source at a systematic neuroscience level. Our goal was to examine the EEG signal associated with the locations from a common network parcellation of the human brain function, offering a system perspective of the influence of EEG reference. In our simulation, vertices uniformly distributed in eight large-scale brain networks were adopted to generate the scalp EEG. The brain networks contain the visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal, default networks, and the deep brain structure. The distributions of the most sensitive and neutral electrodes were calculated for each network based on the lead-field matrix. While the most sensitive electrode had a network-specific symmetric pattern, the electrodes in scalp surface had approximately equal chance to be the most neutral electrode. Simulated data were referenced at the FCz, the Oz, the mean mastoids (MM), the average (AVE), and the infinity reference obtained by the reference electrode standardization technique (REST). Intriguingly, the relative error followed the pattern REST<AVE<MM<(FCz, Oz), regardless of the number of electrodes and signal-to-noise ratios. Our findings suggested that REST was a potentially preferable reference for all large-scale networks and AVE virtually performed as REST under several conditions. As EEG and ERPs experiments within the same behavioral domain always have activations in some specific brain networks, the comparisons revealed here may provide a valuable recommendation for reference selection in clinical and basic researches.

Impact of the reference choice on scalp EEG connectivity estimation

OBJECTIVE

Several scalp EEG functional connectivity studies, mostly clinical, seem to overlook the reference electrode impact. The subsequent interpretation of brain connectivity is thus often biased by the choice of a non-neutral reference. This study aims at systematically investigating these effects.

APPROACH

As EEG reference, we examined the vertex electrode (Cz), the digitally linked mastoids (DLM), the average reference (AVE), and the reference electrode standardization technique (REST). As a connectivity metric, we used the imaginary part of the coherency. We tested simulated and real data (eyes-open resting state) by evaluating the influence of electrode density, the effect of head model accuracy in the REST transformation, and the impact on the characterization of the topology of functional networks from graph analysis.

MAIN RESULTS

Simulations demonstrated that REST significantly reduced the distortion of connectivity patterns when compared to AVE, Cz, and DLM references. Moreover, the availability of high-density EEG systems and an accurate knowledge of the head model are crucial elements to improve REST performance, with the individual realistic head model being preferable to the standard realistic head model. For real data, a systematic change of the spatial pattern of functional connectivity depending on the chosen reference was also observed. The distortion of connectivity patterns was larger for the Cz reference, and progressively decreased when using the DLM, the AVE, and the REST. Strikingly, we also showed that network attributes derived from graph analysis, i.e. node degree and local efficiency, are significantly influenced by the EEG reference choice.

SIGNIFICANCE

Overall, this study highlights that significant differences arise in scalp EEG functional connectivity and graph network properties, in dependence on the chosen reference. We hope that our study will convey the message that caution should be used when interpreting and comparing results obtained from different laboratories using different reference schemes.

Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear

This study presents first evidence that reliable EEG data can be recorded with a new cEEGrid electrode array, which consists of ten electrodes printed on flexible sheet and arranged in a c-shape to fit around the ear. Ten participants wore two cEEGrid systems for at least seven hours. Using a smartphone for stimulus delivery and signal acquisition, resting EEG and auditory oddball data were collected in the morning and in the afternoon six to seven hours apart. Analysis of resting EEG data confirmed well-known spectral differences between eyes open and eyes closed conditions. The ERP results confirmed the predicted condition effects with significantly larger P300 amplitudes for target compared to standard tones, and a high test-retest reliability of the P300 amplitude (r > = .74). Moreover, a linear classifier trained on data from the morning session revealed similar performance in classification accuracy for the morning and the afternoon sessions (both > 70%). These findings demonstrate the feasibility of concealed and comfortable brain activity acquisition over many hours.