Active Dry Electrodes Research Articles

Flexible Finger-Shaped Active Dry EEG Electrode With a Configurable Application-Specific Integrated Circuit and Embedded Leadoff Detection

Dealing with the discomfort and complex setting-up of conventional wet electrodes in the electroencephalography (EEG) monitoring for personal daily healthcare, a flexible finger-shaped active dry EEG electrode is developed and tested in this paper. The flexible finger-shaped dry electrodes, made of polydimethylsiloxane (PDMS) and elastic electrically conductive composite (ECC), penetrate through the hair to achieve stable contact with the scalp. To improve the quality of weak signal acquisition, a customized configurable application-specific integrated circuit (ASIC) is integrated using ECC based fabrication process, which amplifies the signal by 100 V/V, detects the lead-off states of electrodes, communicates with the back-end electronic module for configuration and status feedback. The amplifiers have >100 MΩ @ 50 Hz input impedance to match the large interface impedance of dry electrodes. An I2C slave controller with a dedicated wake-up oscillator is introduced for excellent configurability with a reduced number of wires and lower power consumption. Modulus of the proposed electrodes are similar as human tissue, ensuring remarkable user comfort. Besides, the process costs low and mold-casting is suitable for mass production. Over all, the easy-to-use active dry electrode could meet the requirements for daily EEG monitoring.

Extraction of Individual EEG Gamma Frequencies from the Responses to Click-Based Chirp-Modulated Sounds.

Activity in the gamma range is related to many sensory and cognitive processes that are impaired in neuropsychiatric conditions. Therefore, individualized measures of gamma-band activity are considered to be potential markers that reflect the state of networks within the brain. Relatively little has been studied in respect of the individual gamma frequency (IGF) parameter. The methodology for determining the IGF is not well established. In the present work, we tested the extraction of IGFs from electroencephalogram (EEG) data in two datasets where subjects received auditory stimulation consisting of clicks with varying inter-click periods, covering a 30-60 Hz range: in 80 young subjects EEG was recorded with 64 gel-based electrodes; in 33 young subjects, EEG was recorded using three active dry electrodes. IGFs were extracted from either fifteen or three electrodes in frontocentral regions by estimating the individual-specific frequency that most consistently exhibited high phase locking during the stimulation. The method showed overall high reliability of extracted IGFs for all extraction approaches; however, averaging over channels resulted in somewhat higher reliability scores. This work demonstrates that the estimation of individual gamma frequency is possible using a limited number of both the gel and dry electrodes from responses to click-based chirp-modulated sounds.

Comparing the Usability of Alternative EEG Devices to Traditional Electrode Caps for SSVEP-BCI Controlled Assistive Robots.

Despite having the potential to improve the lives of severely paralyzed users, non-invasive Brain Computer Interfaces (BCI) have yet to be integrated into their daily lives. The widespread adoption of BCI-driven assistive technology is hindered by its lacking usability, as both end-users and researchers alike find fault with traditional EEG caps. In this paper, we compare the usability of four EEG recording devices for Steady-State Visually Evoked Potentials (SSVEP)-BCI applications: an EEG cap (active gel electrodes), two headbands (passive gel or active dry electrodes), and two adhesive electrodes placed on each mastoid. Ten able-bodied participants tested each device by completing an 8-target SSVEP paradigm. Setup times were recorded, and participants rated their satisfaction with each device. The EEG cap obtained the best classification accuracies (Median = 98.96%), followed by the gel electrode headband (Median = 93.75%), and the dry electrode headband (Median = 91.14%). The mastoid electrodes obtained classification accuracies close to chance level (Med = 29.69%). Unknowing of the classification accuracy, participants found the mastoid electrodes to be the most comfortable and discrete. The dry electrode headband obtained the lowest user satisfaction score and was criticized for being too uncomfortable. Participants also noted that the EEG cap was too conspicuous. The gel-based headband provided a good trade-off between BCI performance and user satisfaction.

Design of SSVEP Enhancement-Based Brain Computer Interface

Brain computer interface (BCI) system is a communication bridge between the brain and the external device, and it has been rapidly developed in the recent years. Here, steady state visually evoked potential (SSVEP) is one of the most frequently used control methods for BCI due to its advantages of low training requirement and high stability. However, some people present unobvious SSVEP feature at the location of the primary visual cortex, and this will reduce the performance of SSVEP-based BCIs. In this study, a novel field programmable gate array (FPGA)-based brain computer interface with SSVEP enhancement is proposed to improve the above issue. In the proposed system, a SSVEP-enhancement active dry electrode is designed to acquire good quality of electroencephalography (EEG) without conductive gels, and further enhance the local EEG signal. The experimental results show the proposed system can effectively improve the signal-to-noise ratio of SSVEP and the information transfer rate. Moreover, compared with the current SSVEP-based BCIs in the previous studies, the proposed BCI system contains the advantages of local EEG enhancement, wearablility, wireless transmission, front-end BCI translation, and it contains the potential of applying in many BCI applications in daily life.

Impedance and Noise of Passive and Active Dry EEG Electrodes: A Review

Dry electrodes are a promising solution for prolonged EEG signal acquisition, whereas wet electrodes may lose their signal quality in the same situation and require skin preparation for set-up. Here, we review the impedance and noise of passive and active dry EEG electrodes. In addition, we compare noise and input impedance of the EEG amplifiers. As there are multiple definitions of impedance in each EEG system, they are all first defined. Electrodes must be compatible with amplifiers to accurately record EEG signals. This implies that their impedance plays a significant role in amplifier compatibility and affects total input-referred noise. Therefore, we review the impedance and noise of state-of-the-art amplifiers and electrodes. Furthermore, we compare the various structures and materials used and their final impedance to that of wet electrodes. Finally, we compare state-of-the-art electrodes and amplifiers to the standards of the IFCN and IEC80601-2-26. We investigate bottlenecks and propose a guideline for future work on passive and active dry electrodes, as well as EEG amplifiers.

ECG-Quality Assessment of Dry-Electrode Cooperative Sensors.

Classical approaches for measuring high-quality ECG require the use of gel electrodes and individually shielded cables, which limit patient comfort, especially in long-term use. We recently introduced a novel sensing architecture-so-called cooperative sensors-that allow the use of active dry electrodes connected by two unshielded wires. The aim of this work is to qualitatively evaluate an ECG recorded with a dry-electrode cooperative-sensor system. To that end, preliminary observations were made on three healthy subjects. The ECGs were concurrently recorded with cooperative sensors and a gold-standard 12-lead ECG device during a stress test on a stationary bicycle. First experimental measurements demonstrated the reliability of the approach for a wearable 12-lead ECG monitoring system tested in real settings.

Two-Wired Active Spring-Loaded Dry Electrodes for EEG Measurements.

Dry contact electrode-based EEG acquisition is one of the easiest ways to obtain neural information from the human brain, providing many advantages such as rapid installation, and enhanced wearability. However, high contact impedance due to insufficient electrical coupling at the electrode-scalp interface still remains a critical issue. In this paper, a two-wired active dry electrode system is proposed by combining finger-shaped spring-loaded probes and active buffer circuits. The shrinkable probes and bootstrap topology-based buffer circuitry provide reliable electrical coupling with an uneven and hairy scalp and effective input impedance conversion along with low input capacitance. Through analysis of the equivalent circuit model, the proposed electrode was carefully designed by employing off-the-shelf discrete components and a low-noise zero-drift amplifier. Several electrical evaluations such as noise spectral density measurements and input capacitance estimation were performed together with simple experiments for alpha rhythm detection. The experimental results showed that the proposed electrode is capable of clear detection for the alpha rhythm activation, with excellent electrical characteristics such as low-noise of 1.131 μVRMS and 32.3% reduction of input capacitance.

Design of Wearable Headset with Steady State Visually Evoked Potential-Based Brain Computer Interface

Brain–computer interface (BCI) is a system that allows people to communicate directly with external machines via recognizing brain activities without manual operation. However, for most current BCI systems, conventional electroencephalography (EEG) machines and computers are usually required to acquire EEG signal and translate them into control commands, respectively. The sizes of the above machines are usually large, and this increases the limitation for daily applications. Moreover, conventional EEG electrodes also require conductive gels to improve the EEG signal quality. This causes discomfort and inconvenience of use, while the conductive gels may also encounter the problem of drying out during prolonged measurements. In order to improve the above issues, a wearable headset with steady-state visually evoked potential (SSVEP)-based BCI is proposed in this study. Active dry electrodes were designed and implemented to acquire a good EEG signal quality without conductive gels from the hairy site. The SSVEP BCI algorithm was also implemented into the designed field-programmable gate array (FPGA)-based BCI module to translate SSVEP signals into control commands in real time. Moreover, a commercial tablet was used as the visual stimulus device to provide graphic control icons. The whole system was designed as a wearable device to improve convenience of use in daily life, and it could acquire and translate EEG signal directly in the front-end headset. Finally, the performance of the proposed system was validated, and the results showed that it had excellent performance (information transfer rate = 36.08 bits/min).

A Minimally Invasive Low-Power Platform for Real-Time Brain Computer Interaction Based on Canonical Correlation Analysis

A growing trend in human–computer interaction is to integrate computational capabilities into wearable devices, to enable sophisticated and natural interaction modalities. Acting directly by decoding neural activity is a very natural way of interaction and one of the fundamental paradigms of brain computer interfaces (BCIs) as well. In this paper, we present a wearable Internet of Things node designed for BCI spelling. The system is based on visual evoked potentials detection and runs the canonical correlation analysis on a low power microcontroller. Neural data is acquired by an array of electroencephalography active dry electrodes, suitable for a minimally intrusive interface. To evaluate our solution, we optimized the system on eight subjects and tested it on five different subjects for four and eight stimuli, reaching a peak transfer rate of 1.57 b/s, comparable with those achieved by state-of-the-art nonembedded systems. The power consumption of the device is less than 30 mW, resulting in 122 h of operation with a standard 1000-mAh battery.

Signal Quality and Electrode-Skin Impedance Evaluation in the Context of Wearable Electroencephalographic Systems.

Recent advancement in technology has brought about increase in the application areas of wearable electroencephalographic devices. In that, new types of electrodes take place, and particular attention is needed to ensure the required quality of obtained signals. In this study, we evaluate electrode-skin impedance and signal quality for several kinds of electrodes when used in conditions typical for wearable devices. Results suggest that active dry electrode coated with gold alloy is superior while it was challenging to obtain appropriate signal quality when using passive dry electrodes. We also demonstrate electrode-skin impedance measurement using the analog frontend ADS1299, which is suitable for implementation in wearable devices.

A novel Brain Computer Interface for classification of social joint attention in autism and comparison of 3 experimental setups: A feasibility study

BackgroundWe present a novel virtual-reality P300-based Brain Computer Interface (BCI) paradigm using social cues to direct the focus of attention. We combined interactive immersive virtual-reality (VR) technology with the properties of P300 signals in a training tool which can be used in social attention disorders such as autism spectrum disorder (ASD). New methodWe tested the novel social attention training paradigm (P300-based BCI paradigm for rehabilitation of joint-attention skills) in 13 healthy participants, in 3 EEG systems. The more suitable setup was tested online with 4 ASD subjects. Statistical accuracy was assessed based on the detection of P300, using spatial filtering and a Naïve-Bayes classifier. ResultsWe compared: 1 – g.Mobilab+ (active dry-electrodes, wireless transmission); 2 – g.Nautilus (active electrodes, wireless transmission); 3 – V-Amp with actiCAP Xpress dry-electrodes. Significant statistical classification was achieved in all systems. g.Nautilus proved to be the best performing system in terms of accuracy in the detection of P300, preparation time, speed and reported comfort. Proof of concept tests in ASD participants proved that this setup is feasible for training joint attention skills in ASD. Comparison with existing methodsThis work provides a unique combination of ‘easy-to-use’ BCI systems with new technologies such as VR to train joint-attention skills in autism. ConclusionsOur P300 BCI paradigm is feasible for future Phase I/II clinical trials to train joint-attention skills, with successful classification within few trials, online in ASD participants. The g.Nautilus system is the best performing one to use with the developed BCI setup.

An active dry electrode ecg interface circuit for wearable sensors

A high performance active dry electrode interface circuit for ECG signal monitoring in 0.18µm CMOS process is presented in this paper. AC-coupled core amplifier, input impedance boost loop and CMFB (Common-mode Feedback) circuit are applied in this analog front-end circuit. The programmable gain of the core amplifier is set from 12dB to 40dB. With the input impedance boost loop, a constant 2.4GΩ input impedance is achieved (0.1–150Hz @ typical condition). CMFB circuit is utilized to reduce common-mode interference, and the CMRR reaches 85dB with the configuration of ac gain 40dB. In this design, amplifier-based active dry electrode is utilized, which achieves better noise performance than buffer-based active electrode.

High and dry? Comparing active dry EEG electrodes to active and passive wet electrodes.

Dry electrodes are becoming popular for both lab-based and consumer-level electrophysiological-recording technologies because they better afford the ability to move traditional lab-based research into the real world. It is unclear, however, how dry electrodes compare in data quality to traditional electrodes. The current study compared three EEG electrode types: (a) passive-wet electrodes with no onboard amplification, (b) actively amplified, wet electrodes with moderate impedance levels, and low impedance levels, and (c) active-dry electrodes with very high impedance. Participants completed a classic P3 auditory oddball task to elicit characteristic EEG signatures and event-related potentials (ERPs). Across the three electrode types, we compared single-trial noise, average ERPs, scalp topographies, ERP noise, and ERP statistical power as a function of number of trials. We extended past work showing active electrodes' insensitivity to moderate levels of interelectrode impedance when compared to passive electrodes in the same amplifier. Importantly, the new dry electrode system could reliably measure EEG spectra and ERP components comparable to traditional electrode types. As expected, however, dry active electrodes with very high interelectrode impedance exhibited marked increases in single-trial and average noise levels, which decreased statistical power, requiring more trials to detect significant effects. This power decrease must be considered as a trade-off with the ease of application and long-term use. The current results help set constraints on experimental design with novel dry electrodes, and provide important evidence needed to measure brain activity in novel settings and situations.

A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection.

Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.

Evaluation of a low-cost and low-noise active dry electrode for long-term biopotential recording

Wet Ag/AgCl electrodes, although very popular in clinical diagnosis, are not appropriate for expanding applications of wearable biopotential recording systems which are used repetitively and for a long time. Here, the development of a low-cost and low-noise active dry electrode is presented. The performance of the new electrodes was assessed for recording electrocardiogram (ECG) and electroencephalogram (EEG) in comparison with that of typical gel-based electrodes in a series of long-term recording experiments. The ECG signal recorded by these electrodes was well comparable with usual Ag/AgCl electrodes with a correlation up to 99.5% and mean power line noise below 6.0 μVRMS. The active electrodes were also used to measure alpha wave and steady state visual evoked potential by recording EEG. The recorded signals were comparable in quality with signals recorded by standard gel electrodes, suggesting that the designed electrodes can be employed in EEG-based rehabilitation systems and brain-computer interface applications. The mean power line noise in EEG signals recorded by the active electrodes (1.3 μVRMS) was statistically lower than when conventional gold cup electrodes were used (2.0 μVRMS) with a significant level of 0.05, and the new electrodes appeared to be more resistant to the electromagnetic interferences. These results suggest that the developed low-cost electrodes can be used to develop wearable monitoring systems for long-term biopotential recording.

Electrocardiographic body surface potential maps of the QRS of normal children

Electrocardiographic body surface potential maps (BSPM), utilizing 180 active dry electrodes imbedded in an inflatable vest, were obtained in 40 normal children, ages 8 to 18. The potential levels of the maps are displayed as different colors. A qualitative analysis indicated that the onset of right ventricular breakthrough could always be recognized in the upstroke of the QRS by a pseudopod from a right shoulder minimum extending into an anterior maximum, at an average of 24.4 +/- 4.2 msec., for an average QRS duration of 75.0 +/- 7.1 msec. However there was considerable normal variation, particularly in the mid and late QRS. At the time of depolarization of the free walls of the ventricles, the maximum often remained anterior, with an extension posterior, even through the Frank system vectorcardiogram invariably was posterior. Most remarkable was the terminal QRS of the BSPM, where the terminal maximum may be right superior anterior, anterior superior, or right posterior, presumably reflecting the right ventricular outflow tract, the superior septum, or the posterior basal left ventricle.