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EEG Sensors for Biomedical Applications

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

Deadline for manuscript submissions: closed (25 October 2023) | Viewed by 32591

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Prof. Dr. Filippo Zappasodi

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Department of Neuroscience, Imaging and Clinical Sciences, University 'Gabriele d' Annunzio' of Chieti-Pescara, Chieti, Italy
Interests: electroencephalography; magnetoencephalography; brain stimulation; microstates; machine learning; functional connectivity

Prof. Dr. Silvia Comani

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Department of Neuroscience, Imaging and Clinical Sciences, University 'Gabriele d' Annunzio University' of Chieti-Pescara, Chieti, Italy
Interests: biomedical signal processing; electroencephalography; novel EEG technologies; multimodal neuroscience; sports neuroscience; rehabilitation; functional connectivity; hyperbrain studies; graph theory

Prof. Dr. Patrique Fiedler

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Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
Interests: novel sensor technologies; EEG sensors; biosignal processing; machine learning

Special Issue Information

Dear Colleagues,

Electroencephalography (EEG) is one of the few neuroimaging methods with the excellent time resolution required to measure rapid brain signal changes. Recent technological advances in EEG sensors and electronics (e.g., dry electrodes and miniaturized amplifiers) have made EEG systems easy-to-mount and sufficiently lightweight to enable continuous, accurate, and mobile recordings. The hardware portability, the reduced preparation times, the high spatial resolution as well as high and stable signal quality even for prolonged use, the good comfort levels, and the wireless transmission of EEG signals meet the requirements of neuroscience investigations in ecological conditions. In a clinical environment, these new hardware solutions allow patients to be more comfortable, thus permitting long-term monitoring and providing feasible approaches in rehabilitation procedures. In this perspective, the forthcoming Special Issue aims to solicit articles from academic and industrial institutions with original contributions on advances in EEG sensors that could be beneficial for biomedical applications, basic neuroscience, and clinical investigations from neonates to adults.

Prof. Dr. Filippo Zappasodi
Prof. Dr. Silvia Comani
Prof. Dr. Patrique Fiedler
Guest Editors

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Keywords

mobile EEG
dry sensors
portable EEG device
wearable EEG
wireless EEG
brain–computer interface
sports neuroscience

Published Papers (12 papers)

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20 pages, 7142 KiB

Open AccessArticle

A Film Electrode upon Nanoarchitectonics of Bacterial Cellulose and Conductive Fabric for Forehead Electroencephalogram Measurement

by Kunpeng Gao, Nailong Wu, Bowen Ji and Jingquan Liu

Sensors 2023, 23(18), 7887; https://doi.org/10.3390/s23187887 - 14 Sep 2023

Cited by 3 | Viewed by 789

Abstract

In this paper, we present a soft and moisturizing film electrode based on bacterial cellulose and Ag/AgCl conductive cloth as a potential replacement for gel electrode patches in electroencephalogram (EEG) recording. The electrode materials are entirely flexible, and the bacterial cellulose membrane facilitates convenient adherence to the skin. EEG signals are transmitted from the skin to the bacterial cellulose first and then transferred to the Ag/AgCl conductive cloth connected to the amplifier. The water in the bacterial cellulose moisturizes the skin continuously, reducing the contact impedance to less than 10 kΩ, which is lower than commercial gel electrode patches. The contact impedance and equivalent circuits indicate that the bacterial cellulose electrode effectively reduces skin impedance. Moreover, the bacterial cellulose electrode exhibits lower noise than the gel electrode patch. The bacterial cellulose electrode has demonstrated success in collecting α rhythms. When recording EEG signals, the bacterial cellulose electrode and gel electrode have an average coherence of 0.86, indicating that they have similar performance across different EEG bands. Compared with current mainstream conductive rubber dry electrodes, gel electrodes, and conductive cloth electrodes, the bacterial cellulose electrode has obvious advantages in terms of contact impedance. The bacterial cellulose electrode does not cause skin discomfort after long-term recording, making it more suitable for applications with strict requirements for skin affinity than gel electrode patches. Full article

(This article belongs to the Special Issue EEG Sensors for Biomedical Applications)

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16 pages, 3416 KiB

Open AccessArticle

LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-Based Features with Electroencephalogram Signals

by Orhan Atila, Erkan Deniz, Ali Ari, Abdulkadir Sengur, Subrata Chakraborty, Prabal Datta Barua and U. Rajendra Acharya

Sensors 2023, 23(16), 7032; https://doi.org/10.3390/s23167032 - 08 Aug 2023

Cited by 2 | Viewed by 1133

Abstract

Anxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis of an EEG is highly challenging as it is time-consuming, nonlinear, and nonstationary in nature. Thus, in this paper, a novel approach (LSGP-USFNet) is developed based on the patterns obtained from Ulam’s spiral and Sophia Germain’s prime numbers. The EEG signals are initially filtered to remove the noise and segmented with a non-overlapping sliding window of a length of 512 samples. Then, a time–frequency analysis approach, namely continuous wavelet transform, is applied to each channel of the segmented EEG signal to interpret it in the time and frequency domain. The obtained time–frequency representation is saved as a time–frequency image, and a non-overlapping n × n sliding window is applied to this image for patch extraction. An n × n Ulam’s spiral is localized on each patch, and the gray levels are acquired from this patch as features where Sophie Germain’s primes are located in Ulam’s spiral. All gray tones from all patches are concatenated to construct the features for ADHD and normal classes. A gray tone selection algorithm, namely ReliefF, is employed on the representative features to acquire the final most important gray tones. The support vector machine classifier is used with a 10-fold cross-validation criteria. Our proposed approach, LSGP-USFNet, was developed using a publicly available dataset and obtained an accuracy of 97.46% in detecting ADHD automatically. Our generated model is ready to be validated using a bigger database and it can also be used to detect other children’s neurological disorders. Full article

(This article belongs to the Special Issue EEG Sensors for Biomedical Applications)

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14 pages, 29162 KiB

Open AccessArticle

Fully 3D-Printed Dry EEG Electrodes

by Adele Tong, Praneeth Perera, Zhanna Sarsenbayeva, Alistair McEwan, Anjula C. De Silva and Anusha Withana

Sensors 2023, 23(11), 5175; https://doi.org/10.3390/s23115175 - 29 May 2023

Cited by 2 | Viewed by 1996

Abstract

Electroencephalography (EEG) is used to detect brain activity by recording electrical signals across various points on the scalp. Recent technological advancement has allowed brain signals to be monitored continuously through the long-term usage of EEG wearables. However, current EEG electrodes are not able to cater to different anatomical features, lifestyles, and personal preferences, suggesting the need for customisable electrodes. Despite previous efforts to create customisable EEG electrodes through 3D printing, additional processing after printing is often needed to achieve the required electrical properties. Although fabricating EEG electrodes entirely through 3D printing with a conductive material would eliminate the need for further processing, fully 3D-printed EEG electrodes have not been seen in previous studies. In this study, we investigate the feasibility of using a low-cost setup and a conductive filament, Multi3D Electrifi, to 3D print EEG electrodes. Our results show that the contact impedance between the printed electrodes and an artificial phantom scalp is under 550

Ω

, with phase change of smaller than −30

^{\circ}

, for all design configurations for frequencies ranging from 20 Hz to 10 kHz. In addition, the difference in contact impedance between electrodes with different numbers of pins is under 200

Ω

for all test frequencies. Through a preliminary functional test that monitored the alpha signals (7–13 Hz) of a participant in eye-open and eye-closed states, we show that alpha activity can be identified using the printed electrodes. This work demonstrates that fully 3D-printed electrodes have the capability of acquiring relatively high-quality EEG signals. Full article

(This article belongs to the Special Issue EEG Sensors for Biomedical Applications)

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11 pages, 1674 KiB

Open AccessArticle

Effects of Muscle Fatigue and Recovery on the Neuromuscular Network after an Intermittent Handgrip Fatigue Task: Spectral Analysis of Electroencephalography and Electromyography Signals

by Lin-I Hsu, Kai-Wen Lim, Ying-Hui Lai, Chen-Sheng Chen and Li-Wei Chou

Sensors 2023, 23(5), 2440; https://doi.org/10.3390/s23052440 - 22 Feb 2023

Cited by 2 | Viewed by 2092

Abstract

Mechanisms underlying exercise-induced muscle fatigue and recovery are dependent on peripheral changes at the muscle level and improper control of motoneurons by the central nervous system. In this study, we analyzed the effects of muscle fatigue and recovery on the neuromuscular network through the spectral analysis of electroencephalography (EEG) and electromyography (EMG) signals. A total of 20 healthy right-handed volunteers performed an intermittent handgrip fatigue task. In the prefatigue, postfatigue, and postrecovery states, the participants contracted a handgrip dynamometer with sustained 30% maximal voluntary contractions (MVCs); EEG and EMG data were recorded. A considerable decrease was noted in EMG median frequency in the postfatigue state compared with the findings in other states. Furthermore, the EEG power spectral density of the right primary cortex exhibited a prominent increase in the gamma band. Muscle fatigue led to increases in the beta and gamma bands of contralateral and ipsilateral corticomuscular coherence, respectively. Moreover, a decrease was noted in corticocortical coherence between the bilateral primary motor cortices after muscle fatigue. EMG median frequency may serve as an indicator of muscle fatigue and recovery. Coherence analysis revealed that fatigue reduced the functional synchronization among bilateral motor areas but increased that between the cortex and muscle. Full article

(This article belongs to the Special Issue EEG Sensors for Biomedical Applications)

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16 pages, 4240 KiB

Open AccessArticle

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

by Chuen Rue Ng, Patrique Fiedler, Levin Kuhlmann, David Liley, Beatriz Vasconcelos, Carlos Fonseca, Gabriella Tamburro, Silvia Comani, Troby Ka-Yan Lui, Chun-Yu Tse, Indhika Fauzhan Warsito, Eko Supriyanto and Jens Haueisen

Sensors 2022, 22(20), 8079; https://doi.org/10.3390/s22208079 - 21 Oct 2022

Cited by 9 | Viewed by 3047

Abstract

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. Full article

(This article belongs to the Special Issue EEG Sensors for Biomedical Applications)

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16 pages, 5312 KiB

Open AccessArticle

Developing Disposable EEG Cap for Infant Recordings at the Neonatal Intensive Care Unit

by Amirreza Asayesh, Elina Ilen, Marjo Metsäranta and Sampsa Vanhatalo

Sensors 2022, 22(20), 7869; https://doi.org/10.3390/s22207869 - 16 Oct 2022

Cited by 2 | Viewed by 2466

Abstract

Long-term EEG monitoring in neonatal intensive care units (NICU) is challenged with finding solutions for setting up and maintaining a sufficient recording quality with limited technical experience. The current study evaluates different solutions for the skin–electrode interface and develops a disposable EEG cap for newborn infants. Several alternative materials for the skin–electrode interface were compared to the conventional gel and paste: conductive textiles (textured and woven), conductive Velcro, sponge, super absorbent hydrogel (SAH), and hydro fiber sheets (HF). The comparisons included the assessment of dehydration and recordings of signal quality (skin interphase impedance and powerline (50 Hz) noise) for selected materials. The test recordings were performed using snap electrodes integrated into a forearm sleeve or a forehead band along with skin–electrode interfaces to mimic an EEG cap with the aim of long-term biosignal recording on unprepared skin. In the hydration test, conductive textiles and Velcro performed poorly. While the SAH and HF remained sufficiently hydrated for over 24 h in an incubator-mimicking environment, the sponge material was dehydrated during the first 12 h. Additionally, the SAH was found to have a fragile structure and was electrically prone to artifacts after 12 h. In the electrical impedance and recording comparisons of muscle activity, the results for thick-layer HF were comparable to the conventional gel on unprepared skin. Moreover, the mechanical instability measured by 1–2 Hz and 1–20 Hz normalized relative power spectrum density was comparable with clinical EEG recordings using subdermal electrodes. The results together suggest that thick-layer HF at the skin–electrode interface is an effective candidate for a preparation-free, long-term recording, with many advantages, such as long-lasting recording quality, easy use, and compatibility with sensitive infant skin contact. Full article

(This article belongs to the Special Issue EEG Sensors for Biomedical Applications)

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17 pages, 3132 KiB

Open AccessArticle

Me-Doped Ti–Me Intermetallic Thin Films Used for Dry Biopotential Electrodes: A Comparative Case Study

by Cláudia Lopes, Patrique Fiedler, Marco Sampaio Rodrigues, Joel Borges, Maurizio Bertollo, Eduardo Alves, Nuno Pessoa Barradas, Silvia Comani, Jens Haueisen and Filipe Vaz

Sensors 2021, 21(23), 8143; https://doi.org/10.3390/s21238143 - 06 Dec 2021

Cited by 4 | Viewed by 3093

Abstract

In a new era for digital health, dry electrodes for biopotential measurement enable the monitoring of essential vital functions outside of specialized healthcare centers. In this paper, a new type of nanostructured titanium-based thin film is proposed, revealing improved biopotential sensing performance and overcoming several of the limitations of conventional gel-based electrodes such as reusability, durability, biocompatibility, and comfort. The thin films were deposited on stainless steel (SS) discs and polyurethane (PU) substrates to be used as dry electrodes, for non-invasive monitoring of body surface biopotentials. Four different Ti–Me (Me = Al, Cu, Ag, or Au) metallic binary systems were prepared by magnetron sputtering. The morphology of the resulting Ti–Me systems was found to be dependent on the chemical composition of the films, specifically on the type and amount of Me. The existence of crystalline intermetallic phases or glassy amorphous structures also revealed a strong influence on the morphological features developed by the different systems. The electrodes were tested in an in-vivo study on 20 volunteers during sports activity, allowing study of the application-specific characteristics of the dry electrodes, based on Ti–Me intermetallic thin films, and evaluation of the impact of the electrode–skin impedance on biopotential sensing. The electrode–skin impedance results support the reusability and the high degree of reliability of the Ti–Me dry electrodes. The Ti–Al films revealed the least performance as biopotential electrodes, while the Ti–Au system provided excellent results very close to the Ag/AgCl reference electrodes. Full article

(This article belongs to the Special Issue EEG Sensors for Biomedical Applications)

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18 pages, 3763 KiB

Open AccessArticle

Validation of Soft Multipin Dry EEG Electrodes

by Janne J.A. Heijs, Ruben Jan Havelaar, Patrique Fiedler, Richard J.A. van Wezel and Tjitske Heida

Sensors 2021, 21(20), 6827; https://doi.org/10.3390/s21206827 - 14 Oct 2021

Cited by 9 | Viewed by 3936

Abstract

Current developments towards multipin, dry electrodes in electroencephalography (EEG) are promising for applications in non-laboratory environments. Dry electrodes do not require the application of conductive gel, which mostly confines the use of gel EEG systems to the laboratory environment. The aim of this study is to validate soft, multipin, dry EEG electrodes by comparing their performance to conventional gel EEG electrodes. Fifteen healthy volunteers performed three tasks, with a 32-channel gel EEG system and a 32-channel dry EEG system: the 40 Hz Auditory Steady-State Response (ASSR), the checkerboard paradigm, and an eyes open/closed task. Within-subject analyses were performed to compare the signal quality in the time, frequency, and spatial domains. The results showed strong similarities between the two systems in the time and frequency domains, with strong correlations of the visual (ρ = 0.89) and auditory evoked potential (ρ = 0.81), and moderate to strong correlations for the alpha band during eye closure (ρ = 0.81–0.86) and the 40 Hz-ASSR power (ρ = 0.66–0.72), respectively. However, delta and theta band power was significantly increased, and the signal-to-noise ratio was significantly decreased for the dry EEG system. Topographical distributions were comparable for both systems. Moreover, the application time of the dry EEG system was significantly shorter (8 min). It can be concluded that the soft, multipin dry EEG system can be used in brain activity research with similar accuracy as conventional gel electrodes. Full article

(This article belongs to the Special Issue EEG Sensors for Biomedical Applications)

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21 pages, 3110 KiB

Open AccessArticle

Tracking of Mental Workload with a Mobile EEG Sensor

by Ekaterina Kutafina, Anne Heiligers, Radomir Popovic, Alexander Brenner, Bernd Hankammer, Stephan M. Jonas, Klaus Mathiak and Jana Zweerings

Sensors 2021, 21(15), 5205; https://doi.org/10.3390/s21155205 - 31 Jul 2021

Cited by 14 | Viewed by 3815

Abstract

The aim of the present investigation was to assess if a mobile electroencephalography (EEG) setup can be used to track mental workload, which is an important aspect of learning performance and motivation and may thus represent a valuable source of information in the evaluation of cognitive training approaches. Twenty five healthy subjects performed a three-level N-back test using a fully mobile setup including tablet-based presentation of the task and EEG data collection with a self-mounted mobile EEG device at two assessment time points. A two-fold analysis approach was chosen including a standard analysis of variance and an artificial neural network to distinguish the levels of cognitive load. Our findings indicate that the setup is feasible for detecting changes in cognitive load, as reflected by alterations across lobes in different frequency bands. In particular, we observed a decrease of occipital alpha and an increase in frontal, parietal and occipital theta with increasing cognitive load. The most distinct levels of cognitive load could be discriminated by the integrated machine learning models with an accuracy of 86%. Full article

(This article belongs to the Special Issue EEG Sensors for Biomedical Applications)

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15 pages, 2636 KiB

Open AccessCommunication

EEG-Based Prediction of the Recovery of Carotid Blood Flow during Cardiopulmonary Resuscitation in a Swine Model

by Heejin Kim, Ki Hong Kim, Ki Jeong Hong, Yunseo Ku, Sang Do Shin and Hee Chan Kim

Sensors 2021, 21(11), 3650; https://doi.org/10.3390/s21113650 - 24 May 2021

Cited by 2 | Viewed by 2424

Abstract

The recovery of cerebral circulation during cardiopulmonary resuscitation (CPR) is important to improve the neurologic outcomes of cardiac arrest patients. To evaluate the feasibility of an electroencephalogram (EEG)-based prediction model as a CPR feedback indicator of high- or low-CBF carotid blood flow (CBF), the frontal EEG and hemodynamic data including CBF were measured during animal experiments with a ventricular fibrillation (VF) swine model. The most significant 10 EEG parameters in the time, frequency and entropy domains were determined by neighborhood component analysis and Student’s t-test for discriminating high- or low-CBF recovery with a division criterion of 30%. As a binary CBF classifier, the performances of logistic regression, support vector machine (SVM), k-nearest neighbor, random forest and multilayer perceptron algorithms were compared with eight-fold cross-validation. The three-order polynomial kernel-based SVM model showed the best accuracy of 0.853. The sensitivity, specificity, F1 score and area under the curve of the SVM model were 0.807, 0.906, 0.853 and 0.909, respectively. An automated CBF classifier derived from non-invasive EEG is feasible as a potential indicator of the CBF recovery during CPR in a VF swine model. Full article

(This article belongs to the Special Issue EEG Sensors for Biomedical Applications)

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9 pages, 4425 KiB

Open AccessCommunication

Decoding Brain Responses to Names and Voices across Different Vigilance States

by Tomasz Wielek, Christine Blume, Malgorzata Wislowska, Renata del Giudice and Manuel Schabus

Sensors 2021, 21(10), 3393; https://doi.org/10.3390/s21103393 - 13 May 2021

Cited by 1 | Viewed by 2685

Abstract

Past research has demonstrated differential responses of the brain during sleep in response especially to variations in paralinguistic properties of auditory stimuli, suggesting they can still be processed “offline”. However, the nature of the underlying mechanisms remains unclear. Here, we therefore used multivariate pattern analyses to directly test the similarities in brain activity among different sleep stages (non-rapid eye movement stages N1-N3, as well as rapid-eye movement sleep REM, and wake). We varied stimulus salience by manipulating subjective (own vs. unfamiliar name) and paralinguistic (familiar vs. unfamiliar voice) salience in 16 healthy sleepers during an 8-h sleep opportunity. Paralinguistic salience (i.e., familiar vs. unfamiliar voice) was reliably decoded from EEG response patterns during both N2 and N3 sleep. Importantly, the classifiers trained on N2 and N3 data generalized to N3 and N2, respectively, suggesting similar processing mode in these states. Moreover, projecting the classifiers’ weights using a forward model revealed similar fronto-central topographical patterns in NREM stages N2 and N3. Finally, we found no generalization from wake to any sleep stage (and vice versa) suggesting that “processing modes” or the overall processing architecture with respect to relevant oscillations and/or networks substantially change from wake to sleep. However, the results point to a single and rather uniform NREM-specific mechanism that is involved in (auditory) salience detection during sleep. Full article

(This article belongs to the Special Issue EEG Sensors for Biomedical Applications)

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9 pages, 1129 KiB

Open AccessLetter

EEG Fingerprints under Naturalistic Viewing Using a Portable Device

by Matteo Fraschini, Miro Meli, Matteo Demuru, Luca Didaci and Luigi Barberini

Sensors 2020, 20(22), 6565; https://doi.org/10.3390/s20226565 - 17 Nov 2020

Cited by 4 | Viewed by 2357

Abstract

The electroencephalogram (EEG) has been proven to be a promising technique for personal identification and verification. Recently, the aperiodic component of the power spectrum was shown to outperform other commonly used EEG features. Beyond that, EEG characteristics may capture relevant features related to emotional states. In this work, we aim to understand if the aperiodic component of the power spectrum, as shown for resting-state experimental paradigms, is able to capture EEG-based subject-specific features in a naturalistic stimuli scenario. In order to answer this question, we performed an analysis using two freely available datasets containing EEG recordings from participants during viewing of film clips that aim to trigger different emotional states. Our study confirms that the aperiodic components of the power spectrum, as evaluated in terms of offset and exponent parameters, are able to detect subject-specific features extracted from the scalp EEG. In particular, our results show that the performance of the system was significantly higher for the film clip scenario if compared with resting-state, thus suggesting that under naturalistic stimuli it is even easier to identify a subject. As a consequence, we suggest a paradigm shift, from task-based or resting-state to naturalistic stimuli, when assessing the performance of EEG-based biometric systems. Full article

(This article belongs to the Special Issue EEG Sensors for Biomedical Applications)

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