scholarly journals Performance evaluation of a new 3D printed dry-contact electrode for EEG signals measurement

2021 ◽  
Vol 24 (1) ◽  
pp. 287
Author(s):  
Aaisha Diaa-Aldeen Abdullah ◽  
Auns Q. Al-Neami
Keyword(s):  
Correlation Coefficient ◽  
Silver Chloride ◽  
Signal To Noise Ratio ◽  
Skin Impedance ◽  
Impedance Change ◽  
Eeg Signals ◽  
Dry Electrode ◽  
Dry Contact ◽  
3D Printed ◽  
Contact Electrode

Traditional wet silver/silver chloride electrodes are used to record electroencephalography (EEG) signals mainly because of their potential repeatability, excellent signal to noise ratio and biocompatibility. This type of electrode is only suitable for conductive glue, which can irritate the skin and cause injury. In addition, as time goes the conductive gel will be dehydrated so the quality of the EEG signal will decrease. To overcome these problems, 3D printed dry-contact electrodes with multi-pins are designed in this work to measure brain signals without prior preparation or gel application. 3D printed electrodes are made from polylactic acids polymer and coated with suitable materials to enhance the conductivity. Electrode-scalp impedance on human was also measured. To evaluate the dry-contact electrode, EEG measurement are performed in subjects and compared with EEG signals acquired by wet electrode by using linear correlation coefficient. Experimentally results showed that the average electrode-skin impedance change of dry electrode in frontal site (9.42-7.25KΩ) and in occipital site (9.56-8.66KΩ). The correlation coefficient between dry and wet electrodes in frontal site (91.4%) and in occipital site (80%). To conclude, the 3D printed dry-contact electrode can be will promising applied on hairy site and provide a promising solutions for long-term monitoring EEG.

scholarly journals Flexible 3D-Printed EEG Electrodes

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1650 ◽  
Author(s):  
Andrei Velcescu ◽  
Alexander Lindley ◽  
Ciro Cursio ◽  
Sammy Krachunov ◽  
Christopher Beach ◽  
...  
Keyword(s):  
Silver Chloride ◽  
Eeg Signals ◽  
Contact Impedance ◽  
3D Printed ◽  
Electrode Contact ◽  
Silver Silver ◽  
Silver Silver Chloride ◽  
First Time ◽  
Chloride Coating ◽  
Different Parts

For electroencephalography (EEG) in haired regions of the head, finger-based electrodes have been proposed in order to part the hair and make a direct contact with the scalp. Previous work has demonstrated 3D-printed fingered electrodes to allow personalisation and different configurations of electrodes to be used for different people or for different parts of the head. This paper presents flexible 3D-printed EEG electrodes for the first time. A flexible 3D printing element is now used, with three different base mechanical structures giving differently-shaped electrodes. To obtain improved sensing performance, the silver coatings used previously have been replaced with a silver/silver-chloride coating. This results in reduced electrode contact impedance and reduced contact noise. Detailed electro-mechanical testing is presented to demonstrate the performance of the operation of the new electrodes, particularly with regards to changes in conductivity under compression, together with on-person tests to demonstrate the recording of EEG signals.

scholarly journals Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG

2019 ◽  
Vol 9 (12) ◽  
pp. 352
Author(s):  
Mohammad Shahbakhti ◽  
Maxime Maugeon ◽  
Matin Beiramvand ◽  
Vaidotas Marozas
Keyword(s):  
Wavelet Transform ◽  
Correlation Coefficient ◽  
Signal To Noise Ratio ◽  
Low Complexity ◽  
Stationary Wavelet Transform ◽  
Eeg Signals ◽  
Real Time Processing ◽  
Eye Blink ◽  
Eye Blinks ◽  
Components Analysis

The electroencephalogram signal (EEG) often suffers from various artifacts and noises that have physiological and non-physiological origins. Among these artifacts, eye blink, due to its amplitude is considered to have the most influence on EEG analysis. In this paper, a low complexity approach based on Stationary Wavelet Transform (SWT) and skewness is proposed to remove eye blink artifacts from EEG signals. The proposed method is compared against Automatic Wavelet Independent Components Analysis (AWICA) and Enhanced AWICA. Normalized Root Mean Square Error (NRMSE), Peak Signal-to-Noise Ratio (PSNR), and correlation coefficient ( ρ ) between filtered and pure EEG signals are utilized to quantify artifact removal performance. The proposed approach shows smaller NRMSE, larger PSNR, and larger correlation coefficient values compared to the other methods. Furthermore, the speed of execution of the proposed method is considerably faster than other methods, which makes it more suitable for real-time processing.

Soft Dry Electrodes for Electrocardiogram with Conductive Silver Nanowires

2014 ◽  
Vol 1685 ◽  
Author(s):  
Amanda Myers ◽  
Yong Zhu
Keyword(s):  
Health Monitoring ◽  
Silver Chloride ◽  
Silver Nanowires ◽  
Skin Irritation ◽  
Hospital Setting ◽  
Skin Impedance ◽  
Ecg Signals ◽  
Dry Electrode ◽  
Silver Silver

ABSTRACTWith increasing attention towards long-term health monitoring, there is a pressing need to create noninvasive sensors that monitor vital bioelectronic signals. Particular importance is placed on measuring electrocardiogram (ECG) signals as heart issues are widespread and can be prevented with the proper warning and care of potential problems. Currently, ECGs are taken in a hospital setting using disposable silver-silver chloride (Ag/AgCl) pre-gelled electrodes. Unfortunately, this cannot translate to a long-term monitoring setting due to the electrolytic gel of the electrodes drying and causing skin irritation. This paper presents a soft, skin-mountable dry electrode based on silver nanowires (AgNWs) for measuring ECG signals that can be used in long-term, wearable health monitoring due to the elimination of the electrolytic gel. The AgNWs are embedded in polydimethylsiloxane (PDMS), which creates a robust design that will not suffer from delamination or cracking problems that can eventually lead to loss of conductivity. The electrode is characterized by electrode-skin impedance as a function of frequency and by the surface resistance as the electrode is stretched. The performance of the dry electrode is evaluated and comparable to that of conventional Ag/AgCl electrodes. The ability of the dry electrode to conform to skin is believed to compensate for the lack of an electrolytic gel.

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

2021 ◽  
Vol 15 ◽  
Author(s):  
Beatriz Vasconcelos ◽  
Patrique Fiedler ◽  
René Machts ◽  
Jens Haueisen ◽  
Carlos Fonseca
Keyword(s):  
Silver Chloride ◽  
Thermoplastic Polyurethane ◽  
Substrate Material ◽  
The Novel ◽  
Dry Electrodes ◽  
Signal Characteristics ◽  
Dry Electrode ◽  
Dry Contact ◽  
Silver Silver

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.

scholarly journals EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

2019 ◽  
Vol 9 (11) ◽  
pp. 326 ◽  
Author(s):  
Hong Zeng ◽  
Zhenhua Wu ◽  
Jiaming Zhang ◽  
Chen Yang ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speaker Recognition ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Single Subject ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Electroencephalogram Eeg ◽  
Deep Learning Model

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

scholarly journals A Self-Powered and Highly Accurate Vibration Sensor Based on Bouncing-Ball Triboelectric Nanogenerator for Intelligent Ship Machinery Monitoring

Micromachines ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 218
Author(s):  
Taili Du ◽  
Xusheng Zuo ◽  
Fangyang Dong ◽  
Shunqi Li ◽  
Anaeli Elibariki Mtui ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Work Conditions ◽  
Copper Electrode ◽  
Vibration Sensor ◽  
High Signal ◽  
Triboelectric Nanogenerator ◽  
Vibration Exciter ◽  
Bouncing Ball ◽  
3D Printed ◽  
Self Powered

With the development of intelligent ship, types of advanced sensors are in great demand for monitoring the work conditions of ship machinery. In the present work, a self-powered and highly accurate vibration sensor based on bouncing-ball triboelectric nanogenerator (BB-TENG) is proposed and investigated. The BB-TENG sensor consists of two copper electrode layers and one 3D-printed frame filled with polytetrafluoroethylene (PTFE) balls. When the sensor is installed on a vibration exciter, the PTFE balls will continuously bounce between the two electrodes, generating a periodically fluctuating electrical signals whose frequency can be easily measured through fast Fourier transform. Experiments have demonstrated that the BB-TENG sensor has a high signal-to-noise ratio of 34.5 dB with mean error less than 0.05% at the vibration frequency of 10 Hz to 50 Hz which covers the most vibration range of the machinery on ship. In addition, the BB-TENG can power 30 LEDs and a temperature sensor by converting vibration energy into electricity. Therefore, the BB-TENG sensor can be utilized as a self-powered and highly accurate vibration sensor for condition monitoring of intelligent ship machinery.

A Reliable Stretchable Dry Electrode for Monitoring of EEG signals

2021 ◽  
pp. 112727
Author(s):  
Abolfazl Harati ◽  
Amir Jahanshahi
Keyword(s):  
Eeg Signals ◽  
Dry Electrode

scholarly journals Novel dry electrode EEG headbands for home use: Comparing performance and comfort

2020 ◽  
Vol 6 (3) ◽  
pp. 139-142
Author(s):  
Jens Haueisen ◽  
Patrique Fiedler ◽  
Anna Bernhardt ◽  
Ricardo Gonçalves ◽  
Carlos Fonseca
Keyword(s):  
Brain Activity ◽  
Skin Impedance ◽  
Signal Quality ◽  
The Novel ◽  
Application Time ◽  
Dry Electrodes ◽  
Power Spectral ◽  
Dry Electrode ◽  
Increasing Trend ◽  
Eeg Recordings

AbstractMonitoring brain activity at home using electroencephalography (EEG) is an increasing trend for both medical and non-medical applications. Gel-based electrodes are not suitable due to the gel application requiring extensive preparation and cleaning support for the patient or user. Dry electrodes can be applied without prior preparation by the patient or user. We investigate and compare two dry electrode headbands for EEG acquisition: a novel hybrid dual-textile headband comprising multipin and multiwave electrodes and a neoprene-based headband comprising hydrogel and spidershaped electrodes. We compare the headbands and electrodes in terms of electrode-skin impedance, comfort, electrode offset potential and EEG signal quality. We did not observe considerable differences in the power spectral density of EEG recordings. However, the hydrogel electrodes showed considerably increased impedances and offset potentials, limiting their compatibility with many EEG amplifiers. The hydrogel and spider-shaped electrodes required increased adduction, resulting in a lower wearing comfort throughout the application time compared to the novel headband comprising multipin and multiwave electrodes.

Performance Evaluation of a New Dry-Contact Electrode for EEG Measurement

2021 ◽  
Author(s):  
Aaisha Diaa-Aldeen Abdullah ◽  
Auns Q. Al-Neami
Keyword(s):  
Performance Evaluation ◽  
Dry Contact ◽  
Contact Electrode
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