Electroencephalogram Electrodes Research Articles

Accurate digitization of EEG electrode locations by electromagnetic tracking system: The proposed head rotation method and comparison against optical system

Electroencephalogram (EEG) electrode digitization is crucial for accurate EEG source estimation, and several commercial systems are available for this purpose. The present study aimed to evaluate the digitizing accuracy of electromagnetic and optical systems. Additionally, we introduced a novel rotation method for the electromagnetic system and compared its accuracy with the conventional method of electromagnetic and optical systems. In the conventional method, the operator moves around a stationary participant to digitize, while the participant does not move their head or body. In contrast, in our proposed rotation method with an electromagnetic system, the operator rotates the participant sitting on a swivel chair to digitize in a consistent position. We showed high localization accuracy in both the optical and electromagnetic systems, with an average localization error of less than 3.6 mm. Comparisons of the digitization methods revealed that the electromagnetic system demonstrates superior digitizing accuracy compared to the optical system. Notably, the proposed rotational method is the most accurate among the three methods, which can be attributed to the consistent positioning of EEG electrode digitization within the electromagnetic field. Considering the affordability of the electromagnetic system, our findings provide valuable insights for researchers aiming for precise EEG source estimation.•The study compares the accuracy of electromagnetic and optical systems for EEG electrode digitization, introducing a novel rotation method for improved consistency and precision.•The electromagnetic system, especially with the proposed rotation method, achieves superior digitizing accuracy over the optical system.•Highlighting the cost-effectiveness and precision of the electromagnetic system with the rotation method, this research offers significant insights for achieving precise EEG source estimation.

A review: effects of neurofeedback on patients with mild cognitive impairment (MCI), and Alzheimer's disease (AD).

Neurofeedback training (NFT) is a non-invasive method and has been shown to be effective for attention deficit/hyperactivity disorder (ADHD) and various psychiatric disorders. The aim of this paper is to evaluate the effectiveness of NFT for patients with Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) or Vascular Diseases (VD), so that we searched research articles from four databases, using the keywords neurofeedback, elderly, MCI, AD, VD, and dementia. As a result, 13 articles were identified regarding the effectiveness of NFT in patients with MCI and AD. Although each study differed in study design, training protocol, electroencephalogram (EEG) electrode placement, and reward and inhibition frequency bands, all were shown to enhance memory, attention, and other cognitive abilities. Additional well-designed, randomized studies with sufficient power are needed to further confirm the effectiveness of NFT.

Solid Reservoir Reference Electrode

Potentiometric sensing requires a stable sensing electrode that is selectively responsive to an analyte, and a reference electrode (RE) that provides a stable and well-defined reference potential in a liquid electrolytic environment. In sensor applications where compactness and ease of integration are essential characteristics, pseudo-REs in the form of doped polymer semiconductors, bare metal films, or a bare chlorinated silver film are typically used. Bulk glass electrodes, such as the Ag/AgCl electrode, are incompatible with sensor integration.We report here a solid reservoir reference electrode (SRRE) that achieves the performance of traditional immersed wire Ag/AgCl REs in a compact, layered structure. The SRRE is a solid, layered analogue of the Ag/AgCl RE, with a chlorinated silver film as metallic electrode, a solid KCl layer serving as a saturated reservoir of chloride, and a porous polydimethylsiloxane (PDMS) layer acting as membrane for exchange of water and ions. We demonstrate two different method to fabricate the porous PDMS in figure A and B. The characteristics of the SRRE, including impedance, noise, and long-term drift, can be tuned by adjustment of the PDMS membrane permeance and KCl solid reservoir size.We have demonstrated temporal stability of the RE open circuit potential (OCP), with drift less than 0.37 mV over 17 hours in deionized water. When compared against bare chlorinated silver, the SRRE exhibits superior stability of OCP versus changes in electrolyte ion-concentration, including solutions of potassium chloride, sodium chloride and pH buffers. The ease of fabrication enables SRRE integration with graphene ion-sensitive field effect transistors (ISFETs) to achieve entirely solid-contact micro ion sensors. The solid-contact micro ion sensor can simultaneously measure pH and Na+, in volumes as low as 20 μL. Using the same layered structure, we further demonstrated fabricated electrocardiogram (ECG) and electroencephalogram (EEG) electrodes with significantly lower impedance and noise when compared to disposable gel electrodes. Finally, we have shown that the SRRE can function in non-aqueous solventssuch as acetonitrile, demonstrating the wide range of SRRE applications. Figure 1

Electrical stimulation of the ventromedial prefrontal cortex modulates muscle sympathetic nerve activity and blood pressure.

We recently showed that transcranial alternating current stimulation of the dorsolateral prefrontal cortex modulates spontaneous bursts of muscle sympathetic nerve activity, heart rate, and blood pressure (Sesa-Ashton G, Wong R, McCarthy B, Datta S, Henderson LA, Dawood T, Macefield VG. Stimulation of the dorsolateral prefrontal cortex modulates muscle sympathetic nerve activity and blood pressure in humans. Cereb Cortex Comm. 2022:3:2tgac017.). Stimulation was delivered between scalp electrodes placed over the nasion and electroencephalogram (EEG) electrode site F3 (left dorsolateral prefrontal cortex) or F4 (right dorsolateral prefrontal cortex), and therefore the current passed within the anatomical locations underlying the left and right ventromedial prefrontal cortices. Accordingly, we tested the hypothesis that stimulation of the left and right ventromedial prefrontal cortices would also modulate muscle sympathetic nerve activity, although we predicted that this would be weaker than that seen during dorsolateral prefrontal cortex stimulation. We further tested whether stimulation of the right ventromedial prefrontal cortices would cause greater modulation of muscle sympathetic nerve activity, than stimulation of the left ventromedial prefrontal cortices. In 11 individuals, muscle sympathetic nerve activity was recorded via microelectrodes inserted into the right common peroneal nerve, together with continuous blood pressure, electrocardiogram, and respiration. Stimulation was achieved using transcranial alternating current stimulation, +2 to -2mA, 0.08Hz, 100cycles, applied between electrodes placed over the nasion, and EEG electrode site FP1, (left ventromedial prefrontal cortices) or FP2 (right ventromedial prefrontal cortices); for comparison, stimulation was also applied over F4 (right dorsolateral prefrontal cortex). Stimulation of all three cortical sites caused partial entrainment of muscle sympathetic nerve activity to the sinusoidal stimulation, together with modulation of blood pressure and heart rate. We found a significant fall in mean blood pressure of ~6mmHg (P = 0.039) during stimulation of the left ventromedial prefrontal cortices, as compared with stimulation of the right. We have shown, for the first time, that transcranial alternating current stimulation of the ventromedial prefrontal cortices modulates muscle sympathetic nerve activity and blood pressure in awake humans at rest. However, it is unclear if this modulation occurred through the same brain pathways activated during transcranial alternating current stimulation of the dorsolateral prefrontal cortex.

A Negative Emotion Recognition System with Internet of Things-Based Multimodal Biosignal Data

Previous studies to recognize negative emotions for mental healthcare have used heavy equipment directly attaching electroencephalogram (EEG) electrodes to the head, and they have proposed binary classification methods to identify negative emotions. To tackle this problem, we propose a negative emotion recognition system to collect multimodal biosignal data such as five EEG signals from an EEG headset and heart rate, galvanic skin response, and skin temperature from a smart band for classifying multiple negative emotions. This consists of an Android Internet of Things (IoT) application, a oneM2M-compliant IoT server, and a machine learning server. The Android IoT application uploads the biosignal data to the IoT server. By using the biosignal data stored in the IoT server, the machine learning server recognizes the negative emotions of disgust, fear, and sadness using a multiclass support vector machine (SVM) model with a radial basis function kernel. The experimental results demonstrate that the multimodal biosignal data approach achieves 93% accuracy. Moreover, when considering only data from the smart band, the system achieved 98% accuracy by optimizing the hyperparameters of the multiclass SVM model. Based on these results, we plan to develop a metaverse system that detects and expresses negative emotions in real time.

SGCRNN: A ChebNet-GRU fusion model for eeg emotion recognition

The paper proposes a deep learning model based on Chebyshev Network Gated Recurrent Units, which is called Spectral Graph Convolution Recurrent Neural Network, for multichannel electroencephalogram emotion recognition. First, in this paper, an adjacency matrix capturing the local relationships among electroencephalogram channels is established based on the cosine similarity of the spatial locations of electroencephalogram electrodes. The training efficiency is improved by utilizing the computational speed of the cosine distance. This advantage enables our method to have the potential for real-time emotion recognition, allowing for fast and accurate emotion classification in real-time application scenarios. Secondly, the spatial and temporal dependence of the Spectral Graph Convolution Recurrent Neural Network for capturing electroencephalogram sequences is established based on the characteristics of the Chebyshev network and Gated Recurrent Units to extract the spatial and temporal features of electroencephalogram sequences. The proposed model was tested on the publicly accessible dataset DEAP. Its average recognition accuracy is 88%, 89.5%, and 89.7% for valence, arousal, and dominance, respectively. The experiment results demonstrated that the Spectral Graph Convolution Recurrent Neural Network method performed better than current models for electroencephalogram emotion identification. This model has broad applicability and holds potential for use in real-time emotion recognition scenarios.

Hook Fabric Electroencephalography Electrode for Brain Activity Measurement without Shaving the Head.

In this research, novel electroencephalogram (EEG) electrodes were developed to detect high-quality EEG signals without the requirement of conductive gels, skin treatments, or head shaving. These electrodes were created using electrically conductive hook fabric with a resistance of 1 Ω/sq. The pointed hooks of the conductive fabric establish direct contact with the skin and can penetrate through hair. To ensure excellent contact between the hook fabric electrode and the scalp, a knitted-net EEG bridge cap with a bridging effect was employed. The results showed that the hook fabric electrode exhibited lower skin-to-electrode impedance compared to the dry Ag/AgCl comb electrode. Additionally, it collected high-quality signals on par with the standard wet gold cups and commercial dry Ag/AgCl comb electrodes. Moreover, the hook fabric electrode displayed a higher signal-to-noise ratio (33.6 dB) with a 4.2% advantage over the standard wet gold cup electrode. This innovative electrode design eliminates the need for conductive gel and head shaving, offering enhanced flexibility and lightweight characteristics, making it ideal for integration into textile structures and facilitating convenient long-term monitoring.

EEG-Based Emergency Braking Prediction Using Data Ablation and SVM Classification

Contemporary advanced driver assistance system (ADAS) features for semi-autonomous vehicles include braking assistance during collision avoidance. Although pre-collision detection typically relies on sensing systems to enable production vehicles to perceive oncoming road obstacles, the physiological state of the driver is not measured to predict emergency braking. On the other hand, previous driving simulation experiments have demonstrated the ability of regularized linear discriminant analysis (RLDA) to predict pre-collision braking using brain signals from multiple electroencephalogram (EEG) electrodes. In contrast, the current study used EEG data from these previous experiments to determine the quality of support vector machine (SVM) predictions as a first step towards realizing a brain-computer interface for emergency braking. Power spectral density (PSD) features were extracted from the EEG of one electrode to train and evaluate an SVM. Through a novel data ablation analysis, the optimal number of PSD components was determined to optimize model classification quality measured by the area under the curve (AUC). A comparison of the proposed model to the previous RLDA and other machine learning methods indicated that the SVM had a superior AU C. Thus, the proposed model is a candidate for assisting ADASs with pre-collision detection. Moreover, since the proposed model only utilized one electrode, our study potentially contributes to the facilitation of brain-computer interfaces for autonomous vehicles.

Transcranial direct current stimulation to dorsolateral prefrontal cortex improves performance of obstacle avoidance gait task.

We aimed to investigate the effect of dual-task interference between cognitive and obstacle avoidance walking tasks, and the effect of transcranial direct current stimulation (tDCS) on the performance of this cognitive-motor dual task. The healthy young subjects participated in a single task consisting of a three-digit subtraction task (e.g. 783 - 7) or a 15-m track with six 7.5-cm high obstacles. Then, the subjects performed two single tasks simultaneously as dual tasks, before and after sham and anodal tDCS (2 mA, 20 min) to left dorsolateral prefrontal cortex (DLPFC, the F3 region of the 10/20 electroencephalogram electrode placement system). The effect of tDCS on each outcome (number of correct answers, the clearance height above the obstacle, and foot placement position) was analyzed using repeated-measures analysis of variance. Model effects included tDCS (real, sham), time (pre-, post-tDCS), and task (single task, dual task) conditions. A significant difference in the tDCS, time, and task conditions was observed; the correct number of subtraction tasks increased, and the clearance height and the distance between the obstacle and foot decreased in front of the obstacle. Our findings suggest that dual task performance is causally related to left DLPFC activation under complicated walking tasks and tDCS over this cortical area increases overloaded its information processing capacity.

0261 Sleep Disturbance Reliably Predicted by Pain Sensitivity in Sciatic Nerve Crush Injury Model

Abstract Introduction A bi-directional relationship between sleep and neuropathic pain is of interest because of its impact on human health. Numerous studies in humans and animals have examined the association between sleep and neuropathic pain. However, animal studies showed inconsistent results compared to human data. Therefore, validation of an animal model for sleep-pain association research is needed. We examined the animal models to identify a best-fit model predicting sleep disturbances with the level of neuropathic pain. Methods Adult male C57BL/6J mice (n=7 per group) were implanted with electrodes for electroencephalogram (EEG) and electromyogram. After two weeks of recovery, we obtained baseline EEG data for 24 hours and evaluated the paw withdrawal threshold, a measure of pain sensitivity, by the von Frey test. Afterward, sciatic nerve crush injury (SCI) and common peroneal nerve ligation (CPL) were conducted to make the two different neuropathic pain models. Post-surgery sleep was recorded continuously for 24 hours, followed by von Frey tests. EEG recording during the light period and von Frey tests were repeated on post-surgery days 5, 10, and 15. Results Both SCI and CPL models showed lower paw withdrawal thresholds after the peripheral nerve injury. NREM sleep duration on post-surgery day 1 was reduced significantly compared to baseline in both SCI and CPL, but only SCI showed decreased REM sleep. There was a significant positive correlation between paw withdrawal threshold and NREM sleep duration in SCI (P< 0.0001) but not CPL. Also, wake alpha and theta EEG powers were correlated with pain threshold. Conclusion We confirmed that both models of neuropathic pain disturbed sleep patterns. Nevertheless, sleep disturbances were reliably predicted by the pain sensitivity in the SCI model. Hence, we suggest SCI as a reliable pain model for studying the mechanism of sleep-pain association. Support (if any)

Liquid Superspreading on Surface with Microhexagonal Structure Inspired by Rock‐Climbing Fish

The dynamic spreading mechanism of liquid on a specific surface is vital for understanding interface wetting and antifouling. Whereas, how to control the spreading process and accelerate the spreading speed is a major challenge. The rock‐climbing fish is characterized by its alepidote feature that lives in stream habitats dominated by strong currents. The mucus on its body surface plays a vital role in its adherence and maintenance of antifouling and antibacterial properties. However, the rapid, uniform, and efficient spreading mechanism of mucus on the fish body surface remains largely unknown. Herein, it is revealed that the surface of the rock‐climbing fish is overlaid fully by the microhexagonal texture structure. This hexagonal structure shows a superspreading effect on liquid diffusion, resulting from testing with bionic microfabrication inspired by the rock‐climbing fish. It is demonstrated that the microhexagonal‐textured surface can enhance liquid spreading quickly and evenly on the surface by regulating the moving contact line of the liquid. This kind of superspreading mechanism has great potential applications in the antifouling, electroencephalogram electrode interfaces, flexible skin sensors, and interfacial lubrication of underwater surfaces.

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.

Electroencephalogram signal analysis with 1T1R arrays toward high-efficiency brain computer interface

Brain computer interface (BCI) is a promising way for automatic driving and exploring brain functions. As the number of electrodes for electroencephalogram (EEG) acquisition continues to grow, the signal processing capabilities of BCI are facing challenges. Considering the bottlenecks of the Von Neumann architecture, it is increasingly difficult for the traditional digital computing pattern to meet the requirements of the EEG signal processing in terms of power consumption and efficiency. Here, we propose a 1T1R array-based EEG signal analysis system in which the biological likelihood of the memristor is used to efficiently analyze signals in the simulated domain. The identification and classification of EEG signals are achieved experimentally using the memristor array with an average recognition rate of 89.83%. The support vector machine classification implemented by the memristor crossbar array provides a 34.4 times improvement in power efficiency compared to the complementary metal oxide semiconductor-based support vector machine classifier. This work provides new ideas for the application of memristors in BCI.

Radio-frequency induced heating of intra-cranial EEG electrodes: The more the colder?

Many neurological disorders are analyzed and treated with implantable electrodes. Many patients with such electrodes have to undergo MRI examinations – often unrelated to their implant - at the risk of radio-frequency induced heating. The number of electrode contact sites of these implants keeps increasing due to improvements in manufacturing and computational algorithms. Electrode grids with multiple receive channels couple to the RF fields present in MRI, but, due to their proximity, a combination of leads has a coupling response which is not a superposition of the individual leads’ response. To investigate the problem of RF-induced heating of coupled multi-lead implants, temperature mapping was performed on a set of intra-cranial electroencephalogram (icEEG) electrode grid prototypes with increasing number of contact sites (1-16). Additionally, electric field measurements were used to investigate the radio-frequency heating characteristics of the implants in different media combinations, simulating the device being partially immersed inside the patient.MR measurements show RF-induced heating up to 19.6 K for the single electrode, reducing monotonically with larger number of contact sites to a minimum of 0.9 K for the largest grid. The SAR calculated from temperature measurements agrees well with electric field mapping: The same trend is visible for different insertion lengths, however, the energy dissipated by the whole implant varies with the grid size and insertion length. Thus, in the tested circumstances, a larger electrode number either reduced or had a similar risk of RF induced heating, indicating, that the size of electrode grids is a design parameter, which can be used to change an implants RF response and in turn to reduce the risk of RF induced heating and improve the safety of patient with neuro-implants undergoing MRI examinations.

Frontal Electroencephalography Findings in Critically Ill COVID-19 Patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) negatively impacts the central nervous system, and studies using a full montage of electroencephalogram (EEG) electrodes have reported nonspecific EEG patterns associated with coronavirus disease 2019 (COVID-19) infection. The use of this technology is resource-intensive and limited in its implementation. In this descriptive pilot study, we report neurophysiological patterns and the potential prognostic capability of an abbreviated frontal EEG electrode montage in critically ill COVID-19 patients. Patients receiving mechanical ventilation for SARS-CoV-2 respiratory failure were monitored with Sedline Root Devices using EEG electrodes were placed over the forehead. Qualitative EEG assessments were conducted daily. The primary outcome was mortality, and secondary outcomes were duration of endotracheal intubation and lengths of intensive care and hospitalization stay. Twenty-six patients were included in the study, and EEG discontinuity was identified in 22 (84.6%) patients. The limited sample size and patient heterogeneity precluded statistical analysis, but certain patterns were suggested by trends in the data. Survival was 100% (4/4) for those patients in which a discontinuous EEG pattern was not observed. The majority of patients (87.5%, 7/8) demonstrating activity in the low-moderate frequency range (7 to 17 Hz) survived compared with 61.1% (11/18) of those without this observation. The majority of COVID-19 patients showed signs of EEG discontinuity during monitoring with an abbreviated electrode montage. The trends towards worse survival among those with EEG discontinuity support the need for additional studies to investigate these associations in COVID-19 patients.

Aberrant Gamma-Band Oscillations in Mice with Vitamin D Deficiency: Implications on Schizophrenia and its Cognitive Symptoms.

Vitamin D plays an essential role in cognitive functions as well as regulating calcium homeostasis and the immune system. Many epidemiological studies have also shown the close relationship between vitamin D deficiency (VDD) and the risk of schizophrenia. Cortical gamma-band oscillations (GBO) are associated with cognitive functions, such as attention and memory. Patients with schizophrenia show abnormal GBO with increased spontaneous GBO and decreased evoked GBO. However, the direct effect of VDD on GBO remains unknown. Parvalbumin interneurons, which predominantly contribute to the generation of GBO, are surrounded by perineuronal nets (PNN). We sought to investigate the associations among VDD, PNN, and GBO. Here, we injected a viral vector (AAV5-DIO-ChR2-eYFP) into the basal forebrain stereotaxically and implanted electrodes for electroencephalogram (EEG). At baseline, the evoked and spontaneous EEG power at the gamma frequency band was measured in 4-month-old male PV-Cre mice. After six and twenty weeks of vitamin D deficient food administration, the power of GBO was measured in the VDD condition. Next, we injected the chondroitinase ABC (ChABC) enzyme into the frontal cortex to eliminate PNN. We found that the VDD group showed decreased power of both optogenetically- and auditory-evoked GBO, whereas the spontaneous GBO increased. Enzymatic digestion of PNN showed similar changes in GBO. Taken together, we suggest that VDD could result in decreased PNN and, consequently, increase the spontaneous GBO and decrease the evoked GBO, reminiscent of the aberrant GBO in schizophrenia. These results show that VDD might increase the risk of schizophrenia and aggravate the cognitive symptoms of schizophrenia.

Ten-Hour Stable Noninvasive Brain-Computer Interface Realized by Semidry Hydrogel-Based Electrodes.

Noninvasive brain-computer interface (BCI) has been extensively studied from many aspects in the past decade. In order to broaden the practical applications of BCI technique, it is essential to develop electrodes for electroencephalogram (EEG) collection with advanced characteristics such as high conductivity, long-term effectiveness, and biocompatibility. In this study, we developed a silver-nanowire/PVA hydrogel/melamine sponge (AgPHMS) semidry EEG electrode for long-lasting monitoring of EEG signal. Benefiting from the water storage capacity of PVA hydrogel, the electrolyte solution can be continuously released to the scalp-electrode interface during used. The electrolyte solution can infiltrate the stratum corneum and reduce the scalp-electrode impedance to 10 kΩ-15 kΩ. The flexible structure enables the electrode with mechanical stability, increases the wearing comfort, and reduces the scalp-electrode gap to reduce contact impedance. As a result, a long-term BCI application based on measurements of motion-onset visual evoked potentials (mVEPs) shows that the 3-hour BCI accuracy of the new electrode (77% to 100%) is approximately the same as that of conventional electrodes supported by a conductive gel during the first hour. Furthermore, the BCI system based on the new electrode can retain low contact impedance for 10 hours on scalp, which greatly improved the ability of BCI technique.

Emotion recognition model based on CLSTM and channel attention mechanism

In this paper, we propose an emotion recognition model based on convolutional neural network (CNN), long short term memory (LSTM) and channel attention mechanism, aiming at the low classification accuracy of machine learning methods and the uneven spatial distribution of electroencephalogram (EEG) electrodes. This model can effectively integrate the frequency, space and time information of EEG signals, and improve the accuracy of emotion recognition by adding channel attention mechanism after the last convolutional layer of the model. Firstly, construct a 4-dimensional structure representing EEG signals. Then, a CLSTM model structure combining CNN and LSTM is designed. CNN is used to extract frequency and spatial information from 4-dimensional input, and LSTM is used to extract time information. Finally, the channel attention module is added after the last convolutional layer of CLSTM model structure to allocate the weight of different electrodes. In this paper, an emotion recognition model based on CLSTM and channel attention mechanism was proposed from the perspective of integrating the frequency, space and time 3-dimensional information of EEG signals. The average classification accuracy of the model on SEED public data set reached 93.36%, which was significantly improved over the existing CNN and LSTM emotion recognition models.

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 [Formula: see text] or even lower, which is higher than the wet electrode with an impedance of 8 [Formula: see text]. 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.

Non-invasive on-skin sensors for brain machine interfaces with epitaxial graphene

Objective. Brain–machine interfaces are key components for the development of hands-free, brain-controlled devices. Electroencephalogram (EEG) electrodes are particularly attractive for harvesting the neural signals in a non-invasive fashion. Approach. Here, we explore the use of epitaxial graphene (EG) grown on silicon carbide on silicon for detecting the EEG signals with high sensitivity. Main results and significance. This dry and non-invasive approach exhibits a markedly improved skin contact impedance when benchmarked to commercial dry electrodes, as well as superior robustness, allowing prolonged and repeated use also in a highly saline environment. In addition, we report the newly observed phenomenon of surface conditioning of the EG electrodes. The prolonged contact of the EG with the skin electrolytes functionalize the grain boundaries of the graphene, leading to the formation of a thin surface film of water through physisorption and consequently reducing its contact impedance more than three-fold. This effect is primed in highly saline environments, and could be also further tailored as pre-conditioning to enhance the performance and reliability of the EG sensors.