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EEG Signal Processing for Biomedical Applications
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EEG Signal Processing for Biomedical Applications

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 64942

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Dr. Yvonne Tran

E-Mail Website
Guest Editor
Department of Linguistics, Macquarie University Hearing, Macquarie University, Sydney, NSW 2109, Australia
Interests: biomedical signals; psychophysiology; injury; electroencephalography; heart rate variability; machine learning for rehabilitation medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research focused on brain electrical signals derived from the electroencephalogram (EEG) is gaining traction among researchers from the biomedical, psychology, engineering, and computer science fields. EEG signals have great potential for use in biomedical applications for the diagnosis, treatment, and monitoring of conditions that can alter brain activity, such as mental fatigue. Applications for EEG signals have included the monitoring of brain diseases such as epilepsy, brain tumors, head and spinal injuries, and sleep disorders. Controlling the environment with our mind has always been a wish of humankind. Consequently, assistive technology applications using EEG signals such as brain–computer interfaces (BCI) have been the focus of substantial research, providing a platform for hands-free control. Measuring EEG is reliable, relatively cheap, portable, and non-invasive, making it a key methodology for affordable and effective research, as well as a promising clinical and healthcare tool.

The aim of this Special Issue is to contribute to the current developments pertaining to using EEG signals for biomedical applications. We are inviting submissions of original research, as well as review articles, and new development reports in “Using EEG Signals for Biomedical Applications”.

Topics of interest include (but are not limited to) the following:

  • Biomedical applications using EEG signals;
  • Assistive technologies using EEG;
  • Brain–computer interfaces;
  • EEG signal processing;
  • EEG for monitoring;
  • EEG as a biomarker;
  • The influence of conditions such as fatigue on brain activity;
  • EEG and sleep.

Dr. Yvonne Tran
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • EEG signals
  • Biomedical applications
  • Brain activity
  • Brain–computer interface
  • EEG biomarkers
  • Biomonitoring

Published Papers (16 papers)

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Editorial

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3 pages, 186 KiB  
Editorial
EEG Signal Processing for Biomedical Applications
by Yvonne Tran
Sensors 2022, 22(24), 9754; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249754 - 13 Dec 2022
Cited by 1 | Viewed by 1468
Abstract
Electroencephalography (EEG) signals are used widely in clinical and research settings [...] Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)

Research

Jump to: Editorial, Review

24 pages, 8990 KiB  
Article
Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis
by Md Shafayet Hossain, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Sawal Hamid Md Ali, Ahmad Ashrif A. Bakar, Serkan Kiranyaz, Amith Khandakar, Mohammed Alhatou, Rumana Habib and Muhammad Maqsud Hossain
Sensors 2022, 22(9), 3169; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093169 - 21 Apr 2022
Cited by 10 | Viewed by 3173
Abstract
The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is [...] Read more.
The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average  (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average  (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average  and  values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average  (16.55 dB, utilizing db1 wavelet packet) and largest average  (41.40%, using fk8 wavelet packet). The highest average  and  using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average  also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both  and  values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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25 pages, 1936 KiB  
Article
Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals
by Sergio E. Sánchez-Hernández, Ricardo A. Salido-Ruiz, Sulema Torres-Ramos and Israel Román-Godínez
Sensors 2022, 22(8), 3066; https://0-doi-org.brum.beds.ac.uk/10.3390/s22083066 - 16 Apr 2022
Cited by 20 | Viewed by 3222
Abstract
Epilepsy is a disease that decreases the quality of life of patients; it is also among the most common neurological diseases. Several studies have approached the classification and prediction of seizures by using electroencephalographic data and machine learning techniques. A large diversity of [...] Read more.
Epilepsy is a disease that decreases the quality of life of patients; it is also among the most common neurological diseases. Several studies have approached the classification and prediction of seizures by using electroencephalographic data and machine learning techniques. A large diversity of features has been extracted from electroencephalograms to perform classification tasks; therefore, it is important to use feature selection methods to select those that leverage pattern recognition. In this study, the performance of a set of feature selection methods was compared across different classification models; the classification task consisted of the detection of ictal activity from the CHB-MIT and Siena Scalp EEG databases. The comparison was implemented for different feature sets and the number of features. Furthermore, the similarity between selected feature subsets across classification models was evaluated. The best F1-score (0.90) was reported by the K-nearest neighbor along with the CHB-MIT dataset. Results showed that none of the feature selection methods clearly outperformed the rest of the methods, as the performance was notably affected by the classifier, dataset, and feature set. Two of the combinations (classifier/feature selection method) reporting the best results were K-nearest neighbor/support vector machine and random forest/embedded random forest. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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19 pages, 18206 KiB  
Article
Automatic Speech Discrimination Assessment Methods Based on Event-Related Potentials (ERP)
by Pimwipa Charuthamrong, Pasin Israsena, Solaphat Hemrungrojn and Setha Pan-ngum
Sensors 2022, 22(7), 2702; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072702 - 01 Apr 2022
Cited by 1 | Viewed by 2331
Abstract
Speech discrimination is used by audiologists in diagnosing and determining treatment for hearing loss patients. Usually, assessing speech discrimination requires subjective responses. Using electroencephalography (EEG), a method that is based on event-related potentials (ERPs), could provide objective speech discrimination. In this work we [...] Read more.
Speech discrimination is used by audiologists in diagnosing and determining treatment for hearing loss patients. Usually, assessing speech discrimination requires subjective responses. Using electroencephalography (EEG), a method that is based on event-related potentials (ERPs), could provide objective speech discrimination. In this work we proposed a visual-ERP-based method to assess speech discrimination using pictures that represent word meaning. The proposed method was implemented with three strategies, each with different number of pictures and test sequences. Machine learning was adopted to classify between the task conditions based on features that were extracted from EEG signals. The results from the proposed method were compared to that of a similar visual-ERP-based method using letters and a method that is based on the auditory mismatch negativity (MMN) component. The P3 component and the late positive potential (LPP) component were observed in the two visual-ERP-based methods while MMN was observed during the MMN-based method. A total of two out of three strategies of the proposed method, along with the MMN-based method, achieved approximately 80% average classification accuracy by a combination of support vector machine (SVM) and common spatial pattern (CSP). Potentially, these methods could serve as a pre-screening tool to make speech discrimination assessment more accessible, particularly in areas with a shortage of audiologists. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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16 pages, 2330 KiB  
Article
An Exploratory EEG Analysis on the Effects of Virtual Reality in People with Neuropathic Pain Following Spinal Cord Injury
by Yvonne Tran, Philip Austin, Charles Lo, Ashley Craig, James W. Middleton, Paul J. Wrigley and Philip Siddall
Sensors 2022, 22(7), 2629; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072629 - 29 Mar 2022
Cited by 12 | Viewed by 3507
Abstract
Neuropathic pain in people with spinal cord injury is thought to be due to altered central neuronal activity. A novel therapeutic intervention using virtual reality (VR) head-mounted devices was investigated in this study for pain relief. Given the potential links to neuronal activity, [...] Read more.
Neuropathic pain in people with spinal cord injury is thought to be due to altered central neuronal activity. A novel therapeutic intervention using virtual reality (VR) head-mounted devices was investigated in this study for pain relief. Given the potential links to neuronal activity, the aim of the current study was to determine whether use of VR was associated with corresponding changes in electroencephalography (EEG) patterns linked to the presence of neuropathic pain. Using a within-subject, randomised cross-over pilot trial, we compared EEG activity for three conditions: no task eyes open state, 2D screen task and 3D VR task. We found an increase in delta activity in frontal regions for 3D VR with a decrease in theta activity. There was also a consistent decrease in relative alpha band (8–12 Hz) and an increase in low gamma (30–45 Hz) power during 2D screen and 3D VR corresponding, with reduced self-reported pain. Using the nonlinear and non-oscillatory method of extracting fractal dimensions, we found increases in brain complexity during 2D screen and 3D VR. We successfully classified the 3D VR condition from 2D screen and eyes opened no task conditions with an overall accuracy of 80.3%. The findings in this study have implications for using VR applications as a therapeutic intervention for neuropathic pain in people with spinal cord injury. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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15 pages, 2595 KiB  
Article
EEG Evoked Potentials to Repetitive Transcranial Magnetic Stimulation in Normal Volunteers: Inhibitory TMS EEG Evoked Potentials
by Jing Zhou, Adam Fogarty, Kristina Pfeifer, Jordan Seliger and Robert S. Fisher
Sensors 2022, 22(5), 1762; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051762 - 24 Feb 2022
Cited by 5 | Viewed by 4260
Abstract
The impact of repetitive magnetic stimulation (rTMS) on cortex varies with stimulation parameters, so it would be useful to develop a biomarker to rapidly judge effects on cortical activity, including regions other than motor cortex. This study evaluated rTMS-evoked EEG potentials (TEP) after [...] Read more.
The impact of repetitive magnetic stimulation (rTMS) on cortex varies with stimulation parameters, so it would be useful to develop a biomarker to rapidly judge effects on cortical activity, including regions other than motor cortex. This study evaluated rTMS-evoked EEG potentials (TEP) after 1 Hz of motor cortex stimulation. New features are controls for baseline amplitude and comparison to control groups of sham stimulation. We delivered 200 test pulses at 0.20 Hz before and after 1500 treatment pulses at 1 Hz. Sequences comprised AAA = active stimulation with the same coil for test–treat–test phases (n = 22); PPP = realistic placebo coil stimulation for all three phases (n = 10); and APA = active coil stimulation for tests and placebo coil stimulation for treatment (n = 15). Signal processing displayed the evoked EEG waveforms, and peaks were measured by software. ANCOVA was used to measure differences in TEP peak amplitudes in post-rTMS trials while controlling for pre-rTMS TEP peak amplitude. Post hoc analysis showed reduced P60 amplitude in the active (AAA) rTMS group versus the placebo (APA) group. The N100 peak showed a treatment effect compared to the placebo groups, but no pairwise post hoc differences. N40 showed a trend toward increase. Changes were seen in widespread EEG leads, mostly ipsilaterally. TMS-evoked EEG potentials showed reduction of the P60 peak and increase of the N100 peak, both possibly reflecting increased slow inhibition after 1 Hz of rTMS. TMS-EEG may be a useful biomarker to assay brain excitability at a seizure focus and elsewhere, but individual responses are highly variable, and the difficulty of distinguishing merged peaks complicates interpretation. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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19 pages, 3719 KiB  
Article
Complex Pearson Correlation Coefficient for EEG Connectivity Analysis
by Zoran Šverko, Miroslav Vrankić, Saša Vlahinić and Peter Rogelj
Sensors 2022, 22(4), 1477; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041477 - 14 Feb 2022
Cited by 36 | Viewed by 5749
Abstract
In the background of all human thinking—acting and reacting are sets of connections between different neurons or groups of neurons. We studied and evaluated these connections using electroencephalography (EEG) brain signals. In this paper, we propose the use of the complex [...] Read more.
In the background of all human thinking—acting and reacting are sets of connections between different neurons or groups of neurons. We studied and evaluated these connections using electroencephalography (EEG) brain signals. In this paper, we propose the use of the complex Pearson correlation coefficient (CPCC), which provides information on connectivity with and without consideration of the volume conduction effect. Although the Pearson correlation coefficient is a widely accepted measure of the statistical relationships between random variables and the relationships between signals, it is not being used for EEG data analysis. Its meaning for EEG is not straightforward and rarely well understood. In this work, we compare it to the most commonly used undirected connectivity analysis methods, which are phase locking value (PLV) and weighted phase lag index (wPLI). First, the relationship between the measures is shown analytically. Then, it is illustrated by a practical comparison using synthetic and real EEG data. The relationships between the observed connectivity measures are described in terms of the correlation values between them, which are, for the absolute values of CPCC and PLV, not lower that 0.97, and for the imaginary component of CPCC and wPLI—not lower than 0.92, for all observed frequency bands. Results show that the CPCC includes information of both other measures balanced in a single complex-numbered index. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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17 pages, 2697 KiB  
Communication
Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold
by César Covantes-Osuna, Jhonatan B. López, Omar Paredes, Hugo Vélez-Pérez and Rebeca Romo-Vázquez
Sensors 2021, 21(24), 8305; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248305 - 12 Dec 2021
Cited by 6 | Viewed by 2587
Abstract
The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer [...] Read more.
The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu’s version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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15 pages, 2612 KiB  
Article
Low-Dimensional Dynamics of Brain Activity Associated with Manual Acupuncture in Healthy Subjects
by Xinmeng Guo and Jiang Wang
Sensors 2021, 21(22), 7432; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227432 - 09 Nov 2021
Cited by 2 | Viewed by 1736
Abstract
Acupuncture is one of the oldest traditional medical treatments in Asian countries. However, the scientific explanation regarding the therapeutic effect of acupuncture is still unknown. The much-discussed hypothesis it that acupuncture’s effects are mediated via autonomic neural networks; nevertheless, dynamic brain activity involved [...] Read more.
Acupuncture is one of the oldest traditional medical treatments in Asian countries. However, the scientific explanation regarding the therapeutic effect of acupuncture is still unknown. The much-discussed hypothesis it that acupuncture’s effects are mediated via autonomic neural networks; nevertheless, dynamic brain activity involved in the acupuncture response has still not been elicited. In this work, we hypothesized that there exists a lower-dimensional subspace of dynamic brain activity across subjects, underpinning the brain’s response to manual acupuncture stimulation. To this end, we employed a variational auto-encoder to probe the latent variables from multichannel EEG signals associated with acupuncture stimulation at the ST36 acupoint. The experimental results demonstrate that manual acupuncture stimuli can reduce the dimensionality of brain activity, which results from the enhancement of oscillatory activity in the delta and alpha frequency bands induced by acupuncture. Moreover, it was found that large-scale brain activity could be constrained within a low-dimensional neural subspace, which is spanned by the “acupuncture mode”. In each neural subspace, the steady dynamics of the brain in response to acupuncture stimuli converge to topologically similar elliptic-shaped attractors across different subjects. The attractor morphology is closely related to the frequency of the acupuncture stimulation. These results shed light on probing the large-scale brain response to manual acupuncture stimuli. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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20 pages, 5742 KiB  
Article
Functional Connectivity and Frequency Power Alterations during P300 Task as a Result of Amyotrophic Lateral Sclerosis
by Claudia X. Perez-Ortiz, Jose L. Gordillo, Omar Mendoza-Montoya, Javier M. Antelis, Ricardo Caraza and Hector R. Martinez
Sensors 2021, 21(20), 6801; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206801 - 13 Oct 2021
Cited by 6 | Viewed by 1977
Abstract
Amyotrophic Lateral Sclerosis (ALS) is one of the most aggressive neurodegenerative diseases and is now recognized as a multisystem network disorder with impaired connectivity. Further research for the understanding of the nature of its cognitive affections is necessary to monitor and detect the [...] Read more.
Amyotrophic Lateral Sclerosis (ALS) is one of the most aggressive neurodegenerative diseases and is now recognized as a multisystem network disorder with impaired connectivity. Further research for the understanding of the nature of its cognitive affections is necessary to monitor and detect the disease, so this work provides insight into the neural alterations occurring in ALS patients during a cognitive task (P300 oddball paradigm) by measuring connectivity and the power and latency of the frequency-specific EEG activity of 12 ALS patients and 16 healthy subjects recorded during the use of a P300-based BCI to command a robotic arm. For ALS patients, in comparison to Controls, the results (p < 0.05) were: an increment in latency of the peak ERP in the Delta range (OZ) and Alpha range (PO7), and a decreased power in the Beta band among most electrodes; connectivity alterations among all bands, especially in the Alpha band between PO7 and the channels above the motor cortex. The evolution observed over months of an advanced-state patient backs up these findings. These results were used to compute connectivity- and power-based features to discriminate between ALS and Control groups using Support Vector Machine (SVM). Cross-validation achieved a 100% in specificity and 75% in sensitivity, with an overall 89% success. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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23 pages, 1674 KiB  
Article
EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features
by Ala Hag, Dini Handayani, Thulasyammal Pillai, Teddy Mantoro, Mun Hou Kit and Fares Al-Shargie
Sensors 2021, 21(18), 6300; https://0-doi-org.brum.beds.ac.uk/10.3390/s21186300 - 20 Sep 2021
Cited by 18 | Viewed by 4422
Abstract
Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or [...] Read more.
Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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23 pages, 5148 KiB  
Article
Wavelet Ridges in EEG Diagnostic Features Extraction: Epilepsy Long-Time Monitoring and Rehabilitation after Traumatic Brain Injury
by Yury Vladimirovich Obukhov, Ivan Andreevich Kershner, Renata Alekseevna Tolmacheva, Mikhail Vladimirovich Sinkin and Ludmila Alekseevna Zhavoronkova
Sensors 2021, 21(18), 5989; https://0-doi-org.brum.beds.ac.uk/10.3390/s21185989 - 07 Sep 2021
Cited by 6 | Viewed by 2402
Abstract
Interchannel EEG synchronization, as well as its violation, is an important diagnostic sign of a number of diseases. In particular, during an epileptic seizure, such synchronization occurs starting from some pairs of channels up to many pairs in a generalized seizure. Additionally, for [...] Read more.
Interchannel EEG synchronization, as well as its violation, is an important diagnostic sign of a number of diseases. In particular, during an epileptic seizure, such synchronization occurs starting from some pairs of channels up to many pairs in a generalized seizure. Additionally, for example, after traumatic brain injury, the destruction of interneuronal connections occurs, which leads to a violation of interchannel synchronization when performing motor or cognitive tests. Within the framework of a unified approach to the analysis of interchannel EEG synchronization using the ridges of wavelet spectra, two problems were solved. First, the segmentation of the initial data of long-term monitoring of scalp EEG with various artifacts into fragments suspicious of epileptic seizures in order to reduce the total duration of the fragments analyzed by the doctor. Second, assessments of recovery after rehabilitation of cognitive functions in patients with moderate traumatic brain injury. In the first task, the initial EEG was segmented into fragments in which at least two channels were synchronized, and by the adaptive threshold method into fragments with a high value of the EEG power spectral density. Overlapping in time synchronized fragments with fragments of high spectral power density was determined. As a result, the total duration of the fragments for analysis by the doctor was reduced by more than 60 times. In the second task, the network of phase-related EEG channels was determined during the cognitive test before and after rehabilitation. Calculation-logical and spatial-pattern cognitive tests were used. The positive dynamics of rehabilitation was determined during the initialization of interhemispheric connections and connections in the frontal cortex of the brain. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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24 pages, 1611 KiB  
Article
Generalized Deep Learning EEG Models for Cross-Participant and Cross-Task Detection of the Vigilance Decrement in Sustained Attention Tasks
by Alexander Kamrud, Brett Borghetti, Christine Schubert Kabban and Michael Miller
Sensors 2021, 21(16), 5617; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165617 - 20 Aug 2021
Cited by 6 | Viewed by 2710
Abstract
Tasks which require sustained attention over a lengthy period of time have been a focal point of cognitive fatigue research for decades, with these tasks including air traffic control, watchkeeping, baggage inspection, and many others. Recent research into physiological markers of mental fatigue [...] Read more.
Tasks which require sustained attention over a lengthy period of time have been a focal point of cognitive fatigue research for decades, with these tasks including air traffic control, watchkeeping, baggage inspection, and many others. Recent research into physiological markers of mental fatigue indicate that markers exist which extend across all individuals and all types of vigilance tasks. This suggests that it would be possible to build an EEG model which detects these markers and the subsequent vigilance decrement in any task (i.e., a task-generic model) and in any person (i.e., a cross-participant model). However, thus far, no task-generic EEG cross-participant model has been built or tested. In this research, we explored creation and application of a task-generic EEG cross-participant model for detection of the vigilance decrement in an unseen task and unseen individuals. We utilized three different models to investigate this capability: a multi-layer perceptron neural network (MLPNN) which employed spectral features extracted from the five traditional EEG frequency bands, a temporal convolutional network (TCN), and a TCN autoencoder (TCN-AE), with these two TCN models being time-domain based, i.e., using raw EEG time-series voltage values. The MLPNN and TCN models both achieved accuracy greater than random chance (50%), with the MLPNN performing best with a 7-fold CV balanced accuracy of 64% (95% CI: 0.59, 0.69) and validation accuracies greater than random chance for 9 of the 14 participants. This finding demonstrates that it is possible to classify a vigilance decrement using EEG, even with EEG from an unseen individual and unseen task. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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10 pages, 7434 KiB  
Communication
A Long-Lasting Textile-Based Anatomically Realistic Head Phantom for Validation of EEG Electrodes
by Granch Berhe Tseghai, Benny Malengier, Kinde Anlay Fante and Lieva Van Langenhove
Sensors 2021, 21(14), 4658; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144658 - 07 Jul 2021
Cited by 6 | Viewed by 3165
Abstract
During the development of new electroencephalography electrodes, it is important to surpass the validation process. However, maintaining the human mind in a constant state is impossible which in turn makes the validation process very difficult. Besides, it is also extremely difficult to identify [...] Read more.
During the development of new electroencephalography electrodes, it is important to surpass the validation process. However, maintaining the human mind in a constant state is impossible which in turn makes the validation process very difficult. Besides, it is also extremely difficult to identify noise and signals as the input signals are not known. For that reason, many researchers have developed head phantoms predominantly from ballistic gelatin. Gelatin-based material can be used in phantom applications, but unfortunately, this type of phantom has a short lifespan and is relatively heavyweight. Therefore, this article explores a long-lasting and lightweight (−91.17%) textile-based anatomically realistic head phantom that provides comparable functional performance to a gelatin-based head phantom. The result proved that the textile-based head phantom can accurately mimic body-electrode frequency responses which make it suitable for the controlled validation of new electrodes. The signal-to-noise ratio (SNR) of the textile-based head phantom was found to be significantly better than the ballistic gelatin-based head providing a 15.95 dB ± 1.666 (±10.45%) SNR at a 95% confidence interval. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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Review

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26 pages, 1116 KiB  
Review
A Review on Mental Stress Assessment Methods Using EEG Signals
by Rateb Katmah, Fares Al-Shargie, Usman Tariq, Fabio Babiloni, Fadwa Al-Mughairbi and Hasan Al-Nashash
Sensors 2021, 21(15), 5043; https://0-doi-org.brum.beds.ac.uk/10.3390/s21155043 - 26 Jul 2021
Cited by 94 | Viewed by 11818
Abstract
Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess [...] Read more.
Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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25 pages, 1218 KiB  
Review
Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review
by Kai Zhang, Guanghua Xu, Xiaowei Zheng, Huanzhong Li, Sicong Zhang, Yunhui Yu and Renghao Liang
Sensors 2020, 20(21), 6321; https://0-doi-org.brum.beds.ac.uk/10.3390/s20216321 - 05 Nov 2020
Cited by 39 | Viewed by 5772
Abstract
The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the same probability distribution. [...] Read more.
The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the same probability distribution. However, this may be violated in EEG processing. Variations across sessions/subjects result in a deviation of the feature distribution of EEG signals in the same task, which reduces the accuracy of the decoding model for mental tasks. Recently, transfer learning (TL) has shown great potential in processing EEG signals across sessions/subjects. In this work, we reviewed 80 related published studies from 2010 to 2020 about TL application for EEG decoding. Herein, we report what kind of TL methods have been used (e.g., instance knowledge, feature representation knowledge, and model parameter knowledge), describe which types of EEG paradigms have been analyzed, and summarize the datasets that have been used to evaluate performance. Moreover, we discuss the state-of-the-art and future development of TL for EEG decoding. The results show that TL can significantly improve the performance of decoding models across subjects/sessions and can reduce the calibration time of brain–computer interface (BCI) systems. This review summarizes the current practical suggestions and performance outcomes in the hope that it will provide guidance and help for EEG research in the future. Full article
(This article belongs to the Special Issue EEG Signal Processing for Biomedical Applications)
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