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.
This paper presents the modelling and simulation of a protection system for equipment in the oil and gas industry with various RF grids and enclosures against 500 MHz electromagnetic interference (EMI). COMSOL Multiphysics®Modelling software was used in this study. Electric and magnetic fields distributions were determined by using the Generalized Minimal Residual Method (GMRES) which was integrated into COMSOL Multiphysics® Modelling software. Simulation results indicated that larger RF grid size contributed to the higher electric and magnetic field on equipment. Furthermore, without RF grid, electric and magnetic fields on the equipment were increased significantly (up to 100x). The maximum electric and magnetic fields were found to be near resonance enclosure size (299 mm for 500 MHz frequency source). The results showed that the presence of the RF grid for the EMI protection system was essential.
Due to the direct contact between electrode and scalp, dry EEG electrodes are exposed to increased mechanical wear compared to conventional gel-based electrodes. However, state-of-the-art commercial cap systems commonly use permanently fixated electrodes which can lead to downtime of the EEG cap during professional repair and replacement as well as reduced overall lifetime. An easily replaceable EEG electrode would furthermore improve hygiene, especially for newborn and infant applications. We propose a novel replaceable electrode system, consisting of an electrode holder, a snap top, a contact ring fixated inside the electrode holder, and a replaceable electrode. The production process consists of 3D printing, silicone molding, resin casting, and electroless plating. The replaceable electrode system is integrated into a multichannel EEG cap system. A verification study is conducted with 30 volunteers. The operators experienced that the new electrode holder eases adjustment of the electrode to have proper contact with the scalp. During the study, defective electrodes can be replaced without a soldering process. Furthermore, all electrodes stayed in the holder and did not fall off the cap for the whole session. In conclusion, the novel replaceable electrode system is suitable for EEG measurements.
Accurate electrode signal measurement using EEG head caps can only be achieved through sufficient contact or force. A flexible force sensor is required to obtain accurate force measurement underneath EEG head caps. In this study, we evaluate the performance of a capacitive based sensor including its accuracy, repeatability, hysteresis, and stability. The result shows that accuracy error and repeatability error were 3.03±2.8 % and 3.84±2.92 %, respectively. The stability errors were 2.37±0.15 %(10 gram), 2.54±0.00 % (50 gram), 2.37±0.15 % (100 gram), 5.07±1.16 % (150 gram), 7.27±0.39 % (200 gram). The hysteresis error of the sensor was 4.48±0.47 %. Based on the results, the capacitive based force sensor provides sufficiently low errors in accuracy, repeatability, stability, and hysteresis and is thus suitable for measuring adduction force in EEG cap applications
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