Evaluation of flexible multi-claw and multi-channel semi-dry electrodes for evoked electroencephalography recording

Li, Penghai; Huang, Juanjuan; Li, Mingji; Li, Hongji

doi:10.1016/j.sna.2022.113547

Cited by 6 publications

(4 citation statements)

References 46 publications

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“…Moreover, no differences in the characteristics of EEG signals acquired with caps with equidistant or extended ten-twenty layouts were observed. The applied EEG paradigm and signal metrics cover a broad range of signal characteristics allowing a comprehensive assessment of cap and electrode performance in line with metrics used in related previous studies [ 11 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

“…The performance of gel-based and dry EEG solutions has been compared by either sequential or simultaneous acquisition with both systems within application-specific paradigms. However, most comparative studies thus far comprise low numbers of volunteers and have been conducted in optimally controlled environments, such as shielded acquisition chambers [ 23 ] or focused on low-density EEG with dedicated electrode layouts and applications [ 3 , 4 , 5 , 24 , 25 , 26 ], including manual electrode placement and preparation [ 27 , 28 ]. Moreover, an important limitation of the majority of previous studies is the single-center and single-operator approach.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps

Fiedler

Kuhlmann

et al. 2022

Sensors

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Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain–computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps

Fiedler

Kuhlmann

et al. 2022

Sensors

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“…This makes the electrodes uncomfortable, visually noticeable on the head, susceptible to motion and EEG recordings are greatly affected by motion-induced artifacts. [39][40][41][42] On the other hand, bio-inspired sensors with micropillars and gecko-inspired hierarchical structures made of nanomaterialsbased polymer composites can adhere to hairless skin through Van Der Waal's force, but they are not capable of adhering to the hairy scalp. [43][44][45][46][47] This is because the diameter of the human hairs on the scalp is between 40 and 120 μm [48] -much larger than the size of aforementioned high-density micropillars or gecko-inspired structures that are usually only a few micrometers in diameter.…”

Section: Introductionmentioning

confidence: 99%

Adhesive Wearable Sensors for Electroencephalography from Hairy Scalp

Zhang,

Shyam,

Cunningham

et al. 2023

Adv Healthcare Materials

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Electroencephalography has garnered interest for applications in mobile healthcare, human–machine interfaces, and Internet of Things. Conventional electroencephalography relies on wet and dry electrodes. Despite favorable interface impedance of wet electrodes and skin, the application of a large amount of gel at their interface with skin limits the electroencephalography spatial resolution, increases the risk of shorting between electrodes, and makes them unsuited for long‐term mobile recording. In contrast, dry electrodes are better suited for long‐term recordings but susceptible to motion artifacts. In addition, both wet and dry electrodes are non‐adhesive to the hairy scalp and mechanical support, or chemical adhesives are used to hold them in place. Here, a conical microstructure array (CMSA) based sensor made of carbon nanotube‐polydimethylsiloxane composite is reported. The CMSA sensor is fabricated using the innovative, cost‐effective, and scalable method of viscosity‐controlled dip‐pull process. The sensor adheres to the hairy scalp by generating negative pressure in its conical microstructures when it is pressed against scalp. Aided by the application of a trace amount of gel, CMSA sensor establishes good electrical contact with the skin, enabling its applications in mobile electroencephalography over extended periods. Notably, the signal quality of CMSA sensors is comparable to that of medical‐grade wet gel electrodes.

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“…The contact impedance grows considerably with time due to the conductive fluid's fast evaporation rate. However, it could facilitate the development of short circuits between neighboring electrodes [12]. Many effective stretchy electrodes have been developed alongside the rise in flexible electronics [13].…”

Section: Introductionmentioning

confidence: 99%

Nanocomposite-Based Electrode Structures for EEG Signal Acquisition

et al. 2022

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Objective: To fabricate a lightweight, breathable, comfortable, and able to contour to the curvilinear body shape, electrodes built on a flexible substrate are a significant growth in wearable health monitoring. This research aims to create a GNP/FE electrode-based EEG signal acquisition system that is both efficient and inexpensive. Methodology: Three distinct electrode concentrations were developed for EEG signal acquisition, three distinct electrode concentrations (1.5:1.5, 2:1, and 3:0). The high strength-to-weight ratio to form the tribofilm in the fabrication of the electrode will provide good efficiency. The EEG signal is first subjected to a wavelet transform, which serves as a preliminary analysis. The use of biopotential signals in wearable systems as biofeedback or control commands is expected to substantially impact point-of-care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMI), and brain–computer interfaces (BCIs). The graphene oxide (GO), glycerol (GL), and polyvinyl alcohol (PVA) GO/GL/PVA plastic electrodes were measured and compared to that of a commercially available electrode using the biopic equipment. The GO/GL/PVA plastic electrode was able to detect EEG signals satisfactorily after being used for two months, demonstrating good conductivity and lower noise than the commercial electrode. The GO/GL/PVA nanocomposite mixture was put into the electrode mold as soon as it was ready and then rapidly chilled. Results: The quality of an acquired EEG signal could be measured in several ways including by its error percentage, correlation coefficient, and signal-to-noise ratio (SNR). The fabricated electrode yield detection ranged from 0.81 kPa−1 % to 34.90 kPa−1%. The performance was estimated up to the response of 54 ms. Linear heating at the rate of 40 °C per minute was implemented on the sample ranges from 0 °C to 240 °C. During the sample electrode testing in EEG signal analysis, it obtained low impedance with a good quality of signal acquisition when compared to a conventional wet type of electrode. Conclusions: A large database was frequently built from all of the simulated signals in MATLAB code. Through the experiment, all of the required data were collected, checked against all other signals, and proven that they were accurate representations of the intended database. Evidence suggests that graphene nanoplatelets (GNP) hematite (FE2O3) polyvinylidene fluoride (PVDF) GNP/FE2O3@PVDF electrodes with a 3:0 concentration yielded the best outcomes.

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Evaluation of flexible multi-claw and multi-channel semi-dry electrodes for evoked electroencephalography recording

Cited by 6 publications

References 46 publications

Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps

Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps

Adhesive Wearable Sensors for Electroencephalography from Hairy Scalp

Nanocomposite-Based Electrode Structures for EEG Signal Acquisition

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