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Special Issue "EEG Electrodes"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Chemical Sensors".

Deadline for manuscript submissions: closed (30 November 2018).

Special Issue Editors

Guest Editor
Prof. Dr. Fabio Babiloni

University of Rome “La Sapienza”, P.le A. Moro, 5; 00185, Rome, Italy
Website | E-Mail
Interests: cognitive brain activity; industrial neuroscience; brain computer interface
Guest Editor
Prof. Dr. Wanzeng Kong

College of Computer Science,Hangzhou Dianzi University, Hangzhou 310018, China
Website | E-Mail
Phone: (86-571)-86878578
Fax: (86-571)-86878537
Interests: Brain Computer Interface; Wearable Computing
Guest Editor
Dr. Gianluca Di Flumeri

Dept. of Molecular Medicine, Sapienza University of Rome, P.le A. Moro, 5; 00185, Rome, Italy
Website | E-Mail
Phone: +39 0649912223
Interests: brain activity; cognitive neuroscience; EEG; signal processing; brain computer interface

Special Issue Information

Dear Colleagues,

The measurement of electrical activity from the scalp surface (using the electroencephalogram; EEG, or event-related potentials; ERPs) has been consistently applied to determine neuroelectrical correlates of visuo-motor and cognitive activities in humans, over the last three decades. Impressive advancements have been made in the area of EEG signal processes, both in spatial and temporal domains, with the introduction of powerful methodologies (e.g., source localization, functional connectivity estimates, etc.).

On the other hand, the standard “passive” EEG sensors employed for the detection of scalp activities, until a decade ago, were barely innovated since their standardization in the 1950s.

However, in the last decade, the interest in the use of high-resolution EEG (using full coverage of the scalp surface with 64–128 electrodes) poses new challenges relative to the use of new classes of EEG sensors. Such sensors are supposed easy to mount and to achieve a good quality of the collected cerebral EEG activity.

Different classes of EEG sensors have been recently introduced, based on capacity effects (e.g., dry electrodes) or with improved quality for signal collection (active EEG electrodes). In addition to these different classes of sensors, different “low cost” EEG devices that present integrated sensors and data transmission were sold from the shelf by many vendors. New class of devices, not for clinical use, seemed promising since are sold to the general public, but the quality of the sensors they offered has been not completely and reliably assessed.

In this Special Issue of Sensors you are invited to submit papers related to the characterization and comparison of EEG sensors that are actually available from clinical EEG vendors or “non-clinical” vendors. Furthermore, papers related to the characterization of EEG and ERP activities, detected from particular dry or “active” electrodes are also welcomed. Papers describing the performance of innovative classes of EEG sensors are also considered, as well as studies about the behavior of different classes of EEG sensors in different recording situations outside of the laboratory. Additionally, investigations of innovative signal processing algorithms aimed to address critical issues related to these new kinds of electrodes (e.g., low signal-to-noise ratio) are welcomed as well.

The idea of the Special issue Is to provide to the readership with the state-of-the-art of scientific research in the area of EEG and ERP sensors, by providing new ideas and also systematizing comparisons between different classes of EEG sensors (dry vs. active and passive electrodes).

Prof. Dr. Fabio Babiloni
Prof. Dr. Wanzeng Kong
Dr. Gianluca Di Flumeri
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Dry EEG electrodes
  • Active EEG electrodes
  • EEG
  • ERP
  • EP
  • Signal processing

Published Papers (15 papers)

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Research

Open AccessArticle
Flexible 3D-Printed EEG Electrodes
Sensors 2019, 19(7), 1650; https://doi.org/10.3390/s19071650
Received: 16 February 2019 / Revised: 25 March 2019 / Accepted: 4 April 2019 / Published: 6 April 2019
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Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Open AccessArticle
The Dry Revolution: Evaluation of Three Different EEG Dry Electrode Types in Terms of Signal Spectral Features, Mental States Classification and Usability
Sensors 2019, 19(6), 1365; https://doi.org/10.3390/s19061365
Received: 24 January 2019 / Revised: 27 February 2019 / Accepted: 14 March 2019 / Published: 19 March 2019
Cited by 4 | PDF Full-text (3703 KB) | HTML Full-text | XML Full-text
Abstract
One century after the first recording of human electroencephalographic (EEG) signals, EEG has become one of the most used neuroimaging techniques. The medical devices industry is now able to produce small and reliable EEG systems, enabling a wide variety of applications also with [...] Read more.
One century after the first recording of human electroencephalographic (EEG) signals, EEG has become one of the most used neuroimaging techniques. The medical devices industry is now able to produce small and reliable EEG systems, enabling a wide variety of applications also with no-clinical aims, providing a powerful tool to neuroscientific research. However, these systems still suffer from a critical limitation, consisting in the use of wet electrodes, that are uncomfortable and require expertise to install and time from the user. In this context, dozens of different concepts of EEG dry electrodes have been recently developed, and there is the common opinion that they are reaching traditional wet electrodes quality standards. However, although many papers have tried to validate them in terms of signal quality and usability, a comprehensive comparison of different dry electrode types from multiple points of view is still missing. The present work proposes a comparison of three different dry electrode types, selected among the main solutions at present, against wet electrodes, taking into account several aspects, both in terms of signal quality and usability. In particular, the three types consisted in gold-coated single pin, multiple pins and solid-gel electrodes. The results confirmed the great standards achieved by dry electrode industry, since it was possible to obtain results comparable to wet electrodes in terms of signals spectra and mental states classification, but at the same time drastically reducing the time of montage and enhancing the comfort. In particular, multiple-pins and solid-gel electrodes overcome gold-coated single-pin-based ones in terms of comfort. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Open AccessArticle
Monitoring Pilot’s Mental Workload Using ERPs and Spectral Power with a Six-Dry-Electrode EEG System in Real Flight Conditions
Sensors 2019, 19(6), 1324; https://doi.org/10.3390/s19061324
Received: 12 January 2019 / Revised: 24 February 2019 / Accepted: 12 March 2019 / Published: 16 March 2019
Cited by 1 | PDF Full-text (12469 KB) | HTML Full-text | XML Full-text
Abstract
Recent technological progress has allowed the development of low-cost and highly portable brain sensors such as pre-amplified dry-electrodes to measure cognitive activity out of the laboratory. This technology opens promising perspectives to monitor the “brain at work” in complex real-life situations such as [...] Read more.
Recent technological progress has allowed the development of low-cost and highly portable brain sensors such as pre-amplified dry-electrodes to measure cognitive activity out of the laboratory. This technology opens promising perspectives to monitor the “brain at work” in complex real-life situations such as while operating aircraft. However, there is a need to benchmark these sensors in real operational conditions. We therefore designed a scenario in which twenty-two pilots equipped with a six-dry-electrode EEG system had to perform one low load and one high load traffic pattern along with a passive auditory oddball. In the low load condition, the participants were monitoring the flight handled by a flight instructor, whereas they were flying the aircraft in the high load condition. At the group level, statistical analyses disclosed higher P300 amplitude for the auditory target (Pz, P4 and Oz electrodes) along with higher alpha band power (Pz electrode), and higher theta band power (Oz electrode) in the low load condition as compared to the high load one. Single trial classification accuracy using both event-related potentials and event-related frequency features at the same time did not exceed chance level to discriminate the two load conditions. However, when considering only the frequency features computed over the continuous signal, classification accuracy reached around 70% on average. This study demonstrates the potential of dry-EEG to monitor cognition in a highly ecological and noisy environment, but also reveals that hardware improvement is still needed before it can be used for everyday flight operations. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Open AccessArticle
Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification
Sensors 2019, 19(4), 808; https://doi.org/10.3390/s19040808
Received: 25 December 2018 / Revised: 10 February 2019 / Accepted: 13 February 2019 / Published: 16 February 2019
Cited by 1 | PDF Full-text (4861 KB) | HTML Full-text | XML Full-text
Abstract
Electroencephalography (EEG) provides a non-invasive, portable and low-cost way to convert neural signals into electrical signals. Using EEG to monitor people’s cognitive workload means a lot, especially for tasks demanding high attention. Before deep neural networks became a research hotspot, the use of [...] Read more.
Electroencephalography (EEG) provides a non-invasive, portable and low-cost way to convert neural signals into electrical signals. Using EEG to monitor people’s cognitive workload means a lot, especially for tasks demanding high attention. Before deep neural networks became a research hotspot, the use of spectrum information and the common spatial pattern algorithm (CSP) was the most popular method to classify EEG-based cognitive workloads. Recently, spectral maps have been combined with deep neural networks to achieve a final accuracy of 91.1% across four levels of cognitive workload. In this study, a parallel mechanism of spectral feature-enhanced maps is proposed which enhances the expression of structural information that may be compressed by inter- and intra-subject differences. A public dataset and milestone neural networks, such as AlexNet, VGGNet, ResNet, DenseNet are used to measure the effectiveness of this approach. As a result, the classification accuracy is improved from 91.10% to 93.71%. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Open AccessArticle
Correlation and Similarity between Cerebral and Non-Cerebral Electrical Activity for User’s States Assessment
Sensors 2019, 19(3), 704; https://doi.org/10.3390/s19030704
Received: 21 December 2018 / Revised: 30 January 2019 / Accepted: 7 February 2019 / Published: 9 February 2019
Cited by 2 | PDF Full-text (2790 KB) | HTML Full-text | XML Full-text
Abstract
Human tissues own conductive properties, and the electrical activity produced by human organs can propagate throughout the body due to neuro transmitters and electrolytes. Therefore, it might be reasonable to hypothesize correlations and similarities between electrical activities among different parts of the body. [...] Read more.
Human tissues own conductive properties, and the electrical activity produced by human organs can propagate throughout the body due to neuro transmitters and electrolytes. Therefore, it might be reasonable to hypothesize correlations and similarities between electrical activities among different parts of the body. Since no works have been found in this direction, the proposed study aimed at overcoming this lack of evidence and seeking analogies between the brain activity and the electrical activity of non-cerebral locations, such as the neck and wrists, to determine if i) cerebral parameters can be estimated from non-cerebral sites, and if ii) non-cerebral sensors can replace cerebral sensors for the evaluation of the users under specific experimental conditions, such as eyes open or closed. In fact, the use of cerebral sensors requires high-qualified personnel, and reliable recording systems, which are still expensive. Therefore, the possibility to use cheaper and easy-to-use equipment to estimate cerebral parameters will allow making some brain-based applications less invasive and expensive, and easier to employ. The results demonstrated the occurrence of significant correlations and analogies between cerebral and non-cerebral electrical activity. Furthermore, the same discrimination and classification accuracy were found in using the cerebral or non-cerebral sites for the user’s status assessment. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Open AccessArticle
Quality Assessment of Single-Channel EEG for Wearable Devices
Sensors 2019, 19(3), 601; https://doi.org/10.3390/s19030601
Received: 22 January 2019 / Accepted: 28 January 2019 / Published: 31 January 2019
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Abstract
The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise [...] Read more.
The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. The performance of our method were assessed on different databases containing either artificially contaminated or real artefacts recorded with different type of sensors, including wet and dry EEG electrodes. Furthermore, the quality of unlabelled databases was evaluated. For all the studied databases, the proposed method is able to rapidly assess the quality of the EEG signals with an accuracy higher than 90%. The obtained performance suggests that our approach provide an efficient, fast and automated quality assessment of EEG signals from low-cost wearable devices typically composed of a dry single EEG channel. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Open AccessArticle
EEG Classification of Motor Imagery Using a Novel Deep Learning Framework
Sensors 2019, 19(3), 551; https://doi.org/10.3390/s19030551
Received: 30 November 2018 / Revised: 21 January 2019 / Accepted: 22 January 2019 / Published: 29 January 2019
Cited by 4 | PDF Full-text (1901 KB) | HTML Full-text | XML Full-text
Abstract
Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI) are still limited. In this paper, we propose a classification framework for MI electroencephalogram (EEG) signals that combines a convolutional neural network (CNN) architecture with a variational autoencoder (VAE) for classification. The [...] Read more.
Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI) are still limited. In this paper, we propose a classification framework for MI electroencephalogram (EEG) signals that combines a convolutional neural network (CNN) architecture with a variational autoencoder (VAE) for classification. The decoder of the VAE generates a Gaussian distribution, so it can be used to fit the Gaussian distribution of EEG signals. A new representation of input was developed by combining the time, frequency, and channel information from the EEG signal, and the CNN-VAE method was designed and optimized accordingly for this form of input. In this network, the classification of the extracted CNN features is performed via the deep network VAE. Our framework, with an average kappa value of 0.564, outperforms the best classification method in the literature for BCI Competition IV dataset 2b with a 3% improvement. Furthermore, using our own dataset, the CNN-VAE framework also yields the best performance for both three-electrode and five-electrode EEGs and achieves the best average kappa values 0.568 and 0.603, respectively. Our results show that the proposed CNN-VAE method raises performance to the current state of the art. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Open AccessArticle
Single-Trial Cognitive Stress Classification Using Portable Wireless Electroencephalography
Sensors 2019, 19(3), 499; https://doi.org/10.3390/s19030499
Received: 29 November 2018 / Revised: 5 January 2019 / Accepted: 22 January 2019 / Published: 25 January 2019
Cited by 1 | PDF Full-text (3327 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This work used a low-cost wireless electroencephalography (EEG) headset to quantify the human response to different cognitive stress states on a single-trial basis. We used a Stroop-type color–word interference test to elicit mild stress responses in 18 subjects while recording scalp EEG. Signals [...] Read more.
This work used a low-cost wireless electroencephalography (EEG) headset to quantify the human response to different cognitive stress states on a single-trial basis. We used a Stroop-type color–word interference test to elicit mild stress responses in 18 subjects while recording scalp EEG. Signals recorded from thirteen scalp locations were analyzed using an algorithm that computes the root mean square voltages in the theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands immediately following the initiation of Stroop stimuli; the mean of the Teager energy in each of these three bands; and the wideband EEG signal line-length and number of peaks. These computational features were extracted from the EEG signals on thirteen electrodes during each stimulus presentation and used as inputs to logistic regression, quadratic discriminant analysis, and k-nearest neighbor classifiers. Two complementary analysis methodologies indicated classification accuracies over subjects of around 80% on a balanced dataset for the logistic regression classifier when information from all electrodes was taken into account simultaneously. Additionally, we found evidence that stress responses were preferentially time-locked to stimulus presentation, and that certain electrode–feature combinations worked broadly well across subjects to distinguish stress states. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Open AccessArticle
EEG-Based Identity Authentication Framework Using Face Rapid Serial Visual Presentation with Optimized Channels
Sensors 2019, 19(1), 6; https://doi.org/10.3390/s19010006
Received: 30 October 2018 / Revised: 3 December 2018 / Accepted: 10 December 2018 / Published: 20 December 2018
Cited by 2 | PDF Full-text (1975 KB) | HTML Full-text | XML Full-text
Abstract
Electroencephalogram (EEG) signals, which originate from neurons in the brain, have drawn considerable interests in identity authentication. In this paper, a face image-based rapid serial visual presentation (RSVP) paradigm for identity authentication is proposed. This paradigm combines two kinds of biometric trait, face [...] Read more.
Electroencephalogram (EEG) signals, which originate from neurons in the brain, have drawn considerable interests in identity authentication. In this paper, a face image-based rapid serial visual presentation (RSVP) paradigm for identity authentication is proposed. This paradigm combines two kinds of biometric trait, face and EEG, together to evoke more specific and stable traits for authentication. The event-related potential (ERP) components induced by self-face and non-self-face (including familiar and not familiar) are investigated, and significant differences are found among different situations. On the basis of this, an authentication method based on Hierarchical Discriminant Component Analysis (HDCA) and Genetic Algorithm (GA) is proposed to build subject-specific model with optimized fewer channels. The accuracy and stability over time are evaluated to demonstrate the effectiveness and robustness of our method. The averaged authentication accuracy of 94.26% within 6 s can be achieved by our proposed method. For a 30-day averaged time interval, our method can still reach the averaged accuracy of 88.88%. Experimental results show that our proposed framework for EEG-based identity authentication is effective, robust, and stable over time. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Open AccessArticle
Portable Drowsiness Detection through Use of a Prefrontal Single-Channel Electroencephalogram
Sensors 2018, 18(12), 4477; https://doi.org/10.3390/s18124477
Received: 8 November 2018 / Revised: 14 December 2018 / Accepted: 16 December 2018 / Published: 18 December 2018
Cited by 1 | PDF Full-text (1322 KB) | HTML Full-text | XML Full-text
Abstract
Drowsiness detection has been studied in the context of evaluating products, assessing driver alertness, and managing office environments. Drowsiness level can be readily detected through measurement of human brain activity. The electroencephalogram (EEG), a device whose application relies on adhering electrodes to the [...] Read more.
Drowsiness detection has been studied in the context of evaluating products, assessing driver alertness, and managing office environments. Drowsiness level can be readily detected through measurement of human brain activity. The electroencephalogram (EEG), a device whose application relies on adhering electrodes to the scalp, is the primary method used to monitor brain activity. The many electrodes and wires required to perform an EEG place considerable constraints on the movement of users, and the cost of the device limits its availability. For these reasons, conventional EEG devices are not used in practical studies and businesses. Many potential practical applications could benefit from the development of a wire-free, low-priced device; however, it remains to be elucidated whether portable EEG devices can be used to estimate human drowsiness levels and applied within practical research settings and businesses. In this study, we outline the development of a drowsiness detection system that makes use of a low-priced, prefrontal single-channel EEG device and evaluate its performance in an offline analysis and a practical experiment. Firstly, for the development of the system, we compared three feature extraction methods: power spectral density (PSD), autoregressive (AR) modeling, and multiscale entropy (MSE) for detecting characteristics of an EEG. In order to efficiently select a meaningful PSD, we utilized step-wise linear discriminant analysis (SWLDA). Time-averaging and robust-scaling were used to fit the data for pattern recognition. Pattern recognition was performed by a support vector machine (SVM) with a radial basis function (RBF) kernel. The optimal hyperparameters for the SVM were selected by the grind search method so as to increase drowsiness detection accuracy. To evaluate the performance of the detections, we calculated classification accuracy using the SVM through 10-fold cross-validation. Our model achieved a classification accuracy of 72.7% using the PSD with SWLDA and the SVM. Secondly, we conducted a practical study using the system and evaluated its performance in a practical situation. There was a significant difference (* p < 0.05) between the drowsiness-evoked task and concentration-needed task. Our results demonstrate the efficacy of our low-priced portable drowsiness detection system in quantifying drowsy states. We anticipate that our system will be useful to practical studies with aims as diverse as measurement of classroom mental engagement, evaluation of movies, and office environment evaluation. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Open AccessArticle
Influence of Stainless Needle Electrodes and Silver Disk Electrodes over the Interhemispheric Cerebral Coherence Value in Vigil Dogs
Sensors 2018, 18(11), 3990; https://doi.org/10.3390/s18113990
Received: 8 October 2018 / Revised: 12 November 2018 / Accepted: 13 November 2018 / Published: 16 November 2018
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Abstract
Electroencephalography (EEG) is an objective diagnostic tool in the evaluation of cerebral functionality, both in human and veterinary medicine. For EEG acquisition, different types of electrodes are used, as long as they have no impact on the recorded background activity. However, to date, [...] Read more.
Electroencephalography (EEG) is an objective diagnostic tool in the evaluation of cerebral functionality, both in human and veterinary medicine. For EEG acquisition, different types of electrodes are used, as long as they have no impact on the recorded background activity. However, to date, the influence of electrode type on quantitative EEG and cerebral coherence has not been investigated. Twenty EEG traces (ten with needle electrodes and ten with disk electrodes) were recorded from ten mesocephalic vigil dogs in a monopolar montage. Values for interhemispheric coherence for each frequency band were compared between stainless needle and silver disk electrodes traces. Our results show that the values of interhemispheric coherence in vigil dogs are depending of the type of electrodes used in EEG recordings. In the frontal (FP) channel, for delta and theta frequency bands, the registered coherence is significantly higher when stainless needle electrodes are used. Our results might have important consequences in the field of canine neurology and applied neuroscience, as the frontal channel analysis is preferred in aging and behavior studies. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Open AccessArticle
An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299
Sensors 2018, 18(11), 3721; https://doi.org/10.3390/s18113721
Received: 9 October 2018 / Revised: 26 October 2018 / Accepted: 30 October 2018 / Published: 1 November 2018
Cited by 3 | PDF Full-text (2623 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the [...] Read more.
Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Open AccessArticle
The Dynamic EEG Microstates in Mental Rotation
Sensors 2018, 18(9), 2920; https://doi.org/10.3390/s18092920
Received: 26 May 2018 / Revised: 20 August 2018 / Accepted: 30 August 2018 / Published: 3 September 2018
Cited by 1 | PDF Full-text (1661 KB) | HTML Full-text | XML Full-text
Abstract
Mental rotation is generally analyzed based on event-related potential (ERP) in a time domain with several characteristic electrodes, but neglects the whole spatial-temporal brain pattern in the cognitive process which may reflect the underlying cognitive mechanism. In this paper, we mainly proposed an [...] Read more.
Mental rotation is generally analyzed based on event-related potential (ERP) in a time domain with several characteristic electrodes, but neglects the whole spatial-temporal brain pattern in the cognitive process which may reflect the underlying cognitive mechanism. In this paper, we mainly proposed an approach based on microstates to examine the encoding of mental rotation from the spatial-temporal changes of EEG signals. In particular, we collected EEG data from 11 healthy subjects in a mental rotation cognitive task using 12 different stimulus pictures representing left and right hands at various rotational angles. We applied the microstate method to investigate the microstates conveyed by the event-related potential extracted from EEG data during mental rotation, and obtained four microstate modes (referred to as modes A, B, C, D, respectively). Subsequently, we defined several measures, including microstate sequences, topographical map, hemispheric lateralization, and duration of microstate, to characterize the dynamics of microstates during mental rotation. We observed that (1) the microstates sequence had a specified progressing mode, i.e., A B A ; (2) the activation of the right parietal occipital region was stronger than that of the left parietal occipital region according to the hemispheric lateralization of the microstates mode A; and (3) the duration of the second microstates mode A showed the shorter duration in the vertical stimuli, named “angle effect”. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Open AccessArticle
Adaptive Windowing Framework for Surface Electromyogram-Based Pattern Recognition System for Transradial Amputees
Sensors 2018, 18(8), 2402; https://doi.org/10.3390/s18082402
Received: 5 June 2018 / Revised: 9 July 2018 / Accepted: 18 July 2018 / Published: 24 July 2018
Cited by 1 | PDF Full-text (4348 KB) | HTML Full-text | XML Full-text
Abstract
Electromyogram (EMG)-based Pattern Recognition (PR) systems for upper-limb prosthesis control provide promising ways to enable an intuitive control of the prostheses with multiple degrees of freedom and fast reaction times. However, the lack of robustness of the PR systems may limit their usability. [...] Read more.
Electromyogram (EMG)-based Pattern Recognition (PR) systems for upper-limb prosthesis control provide promising ways to enable an intuitive control of the prostheses with multiple degrees of freedom and fast reaction times. However, the lack of robustness of the PR systems may limit their usability. In this paper, a novel adaptive time windowing framework is proposed to enhance the performance of the PR systems by focusing on their windowing and classification steps. The proposed framework estimates the output probabilities of each class and outputs a movement only if a decision with a probability above a certain threshold is achieved. Otherwise (i.e., all probability values are below the threshold), the window size of the EMG signal increases. We demonstrate our framework utilizing EMG datasets collected from nine transradial amputees who performed nine movement classes with Time Domain Power Spectral Descriptors (TD-PSD), Wavelet and Time Domain (TD) feature extraction (FE) methods and a Linear Discriminant Analysis (LDA) classifier. Nonetheless, the concept can be applied to other types of features and classifiers. In addition, the proposed framework is validated with different movement and EMG channel combinations. The results indicate that the proposed framework works well with different FE methods and movement/channel combinations with classification error rates of approximately 13% with TD-PSD FE. Thus, we expect our proposed framework to be a straightforward, yet important, step towards the improvement of the control methods for upper-limb prostheses. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Open AccessArticle
Development of a Modular Board for EEG Signal Acquisition
Sensors 2018, 18(7), 2140; https://doi.org/10.3390/s18072140
Received: 29 May 2018 / Revised: 29 June 2018 / Accepted: 1 July 2018 / Published: 3 July 2018
Cited by 4 | PDF Full-text (5960 KB) | HTML Full-text | XML Full-text
Abstract
The increased popularity of brain-computer interfaces (BCIs) has created a new demand for miniaturized and low-cost electroencephalogram (EEG) acquisition devices for entertainment, rehabilitation, and scientific needs. The lack of scientific analysis for such system design, modularity, and unified validation tends to suppress progress [...] Read more.
The increased popularity of brain-computer interfaces (BCIs) has created a new demand for miniaturized and low-cost electroencephalogram (EEG) acquisition devices for entertainment, rehabilitation, and scientific needs. The lack of scientific analysis for such system design, modularity, and unified validation tends to suppress progress in this field and limit supply for new low-cost device availability. To eliminate this problem, this paper presents the design and evaluation of a compact, modular, battery powered, conventional EEG signal acquisition board based on an ADS1298 analog front-end chip. The introduction of this novel, vertically stackable board allows the EEG scaling problem to be solved by effectively reconfiguring hardware for small or more demanding applications. The ability to capture 16 to 64 EEG channels at sample rates from 250 Hz to 1000 Hz and to transfer raw EEG signal over a Bluetooth or Wi-Fi interface was implemented. Furthermore, simple but effective assessment techniques were used for system evaluation. While conducted tests confirm the validity of the system against official datasheet specifications and for real-world applications, the proposed quality verification methods can be further employed for analyzing other similar EEG devices in the future. With 6.59 microvolts peak-to-peak input referred noise and a −97 dB common mode rejection ratio in 0–70 Hz band, the proposed design can be qualified as a low-cost precision cEEG research device. Full article
(This article belongs to the Special Issue EEG Electrodes)
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Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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