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Advances on EEG-Based Sensing and Imaging

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

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 17759

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Special Issue Editors

Dr. Winko W. An

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Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
Interests: neurodevelopmental disorder; neuroimaging; biomedical signal processing; machine learning; brain–computer interface
Special Issues, Collections and Topics in MDPI journals

Dr. Laurel Gabard-Durnam

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Guest Editor

Department of Psychology, College of Science, Northeastern University, Boston, MA 02115, USA
Interests: neuroplasticity; sensitive periods; EEG; fMRI; neuroimaging
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Dr. Wanze Xie

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Guest Editor

School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
Interests: child behaviors; ECG and EEG/ERP; cortical source localization; fNIRS and MRI neuroimaging techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past decades, electroencephalography (EEG) has received an increasing interest from the neuroscience and neuroengineering community as a tool for a wide range of research, such as cognitive and behavioral neuroscience, psychology and psychiatry, biomarker discovery, and brain–computer interface. This Special Issue of Sensors aims to communicate recent advances in sensing techniques and analytical methods applied in EEG. Authors are invited to submit cutting-edge research on topics including, but not limited to:

Development of EEG sensors and wearable devices, especially for brain–computer interfaces;
Multi-modal sensor fusion (e.g., EEG with fNIRS, fMRI, ECG, eye-tracking, etc.);
Application of signal processing techniques in EEG denoising, especially for data collected during motion and transportation, or from populations with a known characteristic of high noise level (e.g., infants and children);
Discovery of biomarkers in neurological and neurodevelopmental disorders;
Adoption of machine learning and deep learning algorithms in understanding large-scale EEG data;
EEG software development;
EEG source localization and functional connectivity analysis.

Dr. Winko W. An
Dr. Laurel Gabard-Durnam
Dr. Wanze Xie
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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
brain–computer interface
sensor fusion
denoising
neural biomarkers
machine learning
deep learning

Published Papers (9 papers)

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Research

19 pages, 4715 KiB

Open AccessArticle

Image-Evoked Emotion Recognition for Hearing-Impaired Subjects with EEG Signals

by Mu Zhu, Haonan Jin, Zhongli Bai, Zhiwei Li and Yu Song

Sensors 2023, 23(12), 5461; https://doi.org/10.3390/s23125461 - 09 Jun 2023

Cited by 1 | Viewed by 1035

Abstract

In recent years, there has been a growing interest in the study of emotion recognition through electroencephalogram (EEG) signals. One particular group of interest are individuals with hearing impairments, who may have a bias towards certain types of information when communicating with those in their environment. To address this, our study collected EEG signals from both hearing-impaired and non-hearing-impaired subjects while they viewed pictures of emotional faces for emotion recognition. Four kinds of feature matrices, symmetry difference, and symmetry quotient based on original signal and differential entropy (DE) were constructed, respectively, to extract the spatial domain information. The multi-axis self-attention classification model was proposed, which consists of local attention and global attention, combining the attention model with convolution through a novel architectural element for feature classification. Three-classification (positive, neutral, negative) and five-classification (happy, neutral, sad, angry, fearful) tasks of emotion recognition were carried out. The experimental results show that the proposed method is superior to the original feature method, and the multi-feature fusion achieved a good effect in both hearing-impaired and non-hearing-impaired subjects. The average classification accuracy for hearing-impaired subjects and non-hearing-impaired subjects was 70.2% (three-classification) and 50.15% (five-classification), and 72.05% (three-classification) and 51.53% (five-classification), respectively. In addition, by exploring the brain topography of different emotions, we found that the discriminative brain regions of the hearing-impaired subjects were also distributed in the parietal lobe, unlike those of the non-hearing-impaired subjects. Full article

(This article belongs to the Special Issue Advances on EEG-Based Sensing and Imaging)

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14 pages, 1805 KiB

Open AccessArticle

An Attempt to Develop a Model of Brain Waves Using Quantitative Electroencephalography with Closed Eyes in K1 Kickboxing Athletes—Initial Concept

by Łukasz Rydzik, Tomasz Pałka, Ewa Sobiło-Rydzik, Łukasz Tota, Dorota Ambroży, Tadeusz Ambroży, Pavel Ruzbarsky, Wojciech Czarny and Marta Kopańska

Sensors 2023, 23(8), 4136; https://doi.org/10.3390/s23084136 - 20 Apr 2023

Cited by 3 | Viewed by 1579

Abstract

Background: Brain injuries are a common problem in combat sports, especially in disciplines such as kickboxing. Kickboxing is a combat sport that has several variations of competition, with the most contact-oriented fights being carried out under the format of K-1 rules. While these sports require a high level of skill and physical endurance, frequent micro-traumas to the brain can have serious consequences for the health and well-being of athletes. According to studies, combat sports are one of the riskiest sports in terms of brain injuries. Among the sports disciplines with the highest number of brain injuries, boxing, mixed martial arts (MMA), and kickboxing are mentioned. Methods: The study was conducted on a group of 18 K-1 kickboxing athletes who demonstrate a high level of sports performance. The subjects were between the ages 18 and 28. QEEG (quantitative electroencephalogram) is a numeric spectral analysis of the EEG record, where the data is digitally coded and statistically analysed using the Fourier transform algorithm. Each examination of one person lasts about 10 min with closed eyes. The wave amplitude and power for specific frequencies (Delta, Theta, Alpha, Sensorimotor Rhythm (SMR), Beta 1, and Beta2) were analysed using 9 leads. Results: High values were shown in the Alpha frequency for central leads, SMR in the Frontal 4 (F4 lead), Beta 1 in leads F4 and Parietal 3 (P3), and Beta2 in all leads. Conclusions: The high activity of brainwaves such as SMR, Beta and Alpha can have a negative effect on the athletic performance of kickboxing athletes by affecting focus, stress, anxiety, and concentration. Therefore, it is important for athletes to monitor their brainwave activity and use appropriate training strategies to achieve optimal results. Full article

(This article belongs to the Special Issue Advances on EEG-Based Sensing and Imaging)

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19 pages, 7500 KiB

Open AccessArticle

Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals

by Jammisetty Yedukondalu and Lakhan Dev Sharma

Sensors 2023, 23(3), 1235; https://doi.org/10.3390/s23031235 - 21 Jan 2023

Cited by 4 | Viewed by 1684

Abstract

Background: Portable electroencephalogram (EEG) systems are often used in health care applications to record brain signals because their ease of use. An electrooculogram (EOG) is a common, low frequency, high amplitude artifact of the eye blink signal that might confuse disease diagnosis. As a result, artifact removal approaches in single EEG portable devices are in high demand. Materials: Dataset 2a from the BCI Competition IV was employed. It contains the EEG data from nine subjects. To determine the EOG effect, each session starts with 5 min of EEG data. This recording lasted for two minutes with the eyes open, one minute with the eyes closed, and one minute with eye movements. Methodology: This article presents the automated removal of EOG artifacts from EEG signals. Circulant Singular Spectrum Analysis (CiSSA) was used to decompose the EOG contaminated EEG signals into intrinsic mode functions (IMFs). Next, we identified the artifact signal components using kurtosis and energy values and removed them using 4-level discrete wavelet transform (DWT). Results: The proposed approach was evaluated on synthetic and real EEG data and found to be effective in eliminating EOG artifacts while maintaining low frequency EEG information. CiSSA-DWT achieved the best signal to artifact ratio (SAR), mean absolute error (MAE), relative root mean square error (RRMSE), and correlation coefficient (CC) of 1.4525, 0.0801, 18.274, and 0.9883, respectively. Comparison: The developed technique outperforms existing artifact suppression techniques according to performance measures. Conclusions: This advancement is important for brain science and can contribute as an initial pre-processing step for research related to EEG signals. Full article

(This article belongs to the Special Issue Advances on EEG-Based Sensing and Imaging)

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13 pages, 2607 KiB

Open AccessArticle

iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG

by Colton B. Gonsisko, Daniel P. Ferris and Ryan J. Downey

Sensors 2023, 23(2), 928; https://doi.org/10.3390/s23020928 - 13 Jan 2023

Cited by 7 | Viewed by 2074

Abstract

Motion artifacts hinder source-level analysis of mobile electroencephalography (EEG) data using independent component analysis (ICA). iCanClean is a novel cleaning algorithm that uses reference noise recordings to remove noisy EEG subspaces, but it has not been formally tested in a parameter sweep. The goal of this study was to test iCanClean’s ability to improve the ICA decomposition of EEG data corrupted by walking motion artifacts. Our primary objective was to determine optimal settings and performance in a parameter sweep (varying the window length and r² cleaning aggressiveness). High-density EEG was recorded with 120 + 120 (dual-layer) EEG electrodes in young adults, high-functioning older adults, and low-functioning older adults. EEG data were decomposed by ICA after basic preprocessing and iCanClean. Components well-localized as dipoles (residual variance < 15%) and with high brain probability (ICLabel > 50%) were marked as ‘good’. We determined iCanClean’s optimal window length and cleaning aggressiveness to be 4-s and r² = 0.65 for our data. At these settings, iCanClean improved the average number of good components from 8.4 to 13.2 (+57%). Good performance could be maintained with reduced sets of noise channels (12.7, 12.2, and 12.0 good components for 64, 32, and 16 noise channels, respectively). Overall, iCanClean shows promise as an effective method to clean mobile EEG data. Full article

(This article belongs to the Special Issue Advances on EEG-Based Sensing and Imaging)

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9 pages, 931 KiB

Open AccessArticle

Paroxysmal Slow-Wave Events Are Uncommon in Parkinson’s Disease

by Dan Z. Milikovsky, Yotam Sharabi, Nir Giladi, Anat Mirelman, Ronen Sosnik, Firas Fahoum and Inbal Maidan

Sensors 2023, 23(2), 918; https://doi.org/10.3390/s23020918 - 13 Jan 2023

Viewed by 1385

Abstract

Background: Parkinson’s disease (PD) is currently considered to be a multisystem neurodegenerative disease that involves cognitive alterations. EEG slowing has been associated with cognitive decline in various neurological diseases, such as PD, Alzheimer’s disease (AD), and epilepsy, indicating cortical involvement. A novel method revealed that this EEG slowing is composed of paroxysmal slow-wave events (PSWE) in AD and epilepsy, but in PD it has not been tested yet. Therefore, this study aimed to examine the presence of PSWE in PD as a biomarker for cortical involvement. Methods: 31 PD patients, 28 healthy controls, and 18 juvenile myoclonic epilepsy (JME) patients (served as positive control), underwent four minutes of resting-state EEG. Spectral analyses were performed to identify PSWEs in nine brain regions. Mixed-model analysis was used to compare between groups and brain regions. The correlation between PSWEs and PD duration was examined using Spearman’s test. Results: No significant differences in the number of PSWEs were observed between PD patients and controls (p > 0.478) in all brain regions. In contrast, JME patients showed a higher number of PSWEs than healthy controls in specific brain regions (p < 0.023). Specifically in the PD group, we found that a higher number of PSWEs correlated with longer disease duration. Conclusions: This study is the first to examine the temporal characteristics of EEG slowing in PD by measuring the occurrence of PSWEs. Our findings indicate that PD patients who are cognitively intact do not have electrographic manifestations of cortical involvement. However, the correlation between PSWEs and disease duration may support future studies of repeated EEG recordings along the disease course to detect early signs of cortical involvement in PD. Full article

(This article belongs to the Special Issue Advances on EEG-Based Sensing and Imaging)

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11 pages, 1804 KiB

Open AccessArticle

Explainable Machine-Learning-Based Characterization of Abnormal Cortical Activities for Working Memory of Restless Legs Syndrome Patients

by Minju Kim, Hyun Kim, Pukyeong Seo, Ki-Young Jung and Kyung Hwan Kim

Sensors 2022, 22(20), 7792; https://doi.org/10.3390/s22207792 - 14 Oct 2022

Cited by 3 | Viewed by 1418

Abstract

Restless legs syndrome (RLS) is a sensorimotor disorder accompanied by a strong urge to move the legs and an unpleasant sensation in the legs, and is known to accompany prefrontal dysfunction. Here, we aimed to clarify the neural mechanism of working memory deficits associated with RLS using machine-learning-based analysis of single-trial neural activities. A convolutional neural network classifier was developed to discriminate the cortical activities between RLS patients and normal controls. A layer-wise relevance propagation was applied to the trained classifier in order to determine the critical nodes in the input layer for the output decision, i.e., the time/location of cortical activities discriminating RLS patients and normal controls during working memory tasks. Our method provided high classification accuracy (~94%) from single-trial event-related potentials, which are known to suffer from high inter-trial/inter-subject variation and low signal-to-noise ratio, after strict separation of training/test/validation data according to leave-one-subject-out cross-validation. The determined critical areas overlapped with the cortical substrates of working memory, and the neural activities in these areas were correlated with some significant clinical scores of RLS. Full article

(This article belongs to the Special Issue Advances on EEG-Based Sensing and Imaging)

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16 pages, 1045 KiB

Open AccessArticle

An Adaptive Task-Related Component Analysis Method for SSVEP Recognition

by Vangelis P. Oikonomou

Sensors 2022, 22(20), 7715; https://doi.org/10.3390/s22207715 - 11 Oct 2022

Cited by 7 | Viewed by 1353

Abstract

Steady-State Visual Evoked Potential (SSVEP) recognition methods use a subject’s calibration data to differentiate between brain responses, hence, providing the SSVEP-based brain–computer interfaces (BCIs) with high performance. However, they require sufficient calibration EEG trials to achieve that. This study develops a new method to learn from limited calibration EEG trials, and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEP detection. The spatial filter learned from each stimulus utilizes temporal information from the corresponding EEG trials. To introduce the temporal information into the overall procedure, a multitask learning approach, based on the Bayesian framework, is adopted. The performance of the proposed method was evaluated into two publicly available benchmark datasets, and the results demonstrated that our method outperformed competing methods by a significant margin. Full article

(This article belongs to the Special Issue Advances on EEG-Based Sensing and Imaging)

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17 pages, 3181 KiB

Open AccessArticle

Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis

by Amanda Studnicki, Ryan J. Downey and Daniel P. Ferris

Sensors 2022, 22(15), 5867; https://doi.org/10.3390/s22155867 - 05 Aug 2022

Cited by 14 | Viewed by 3624

Abstract

Researchers can improve the ecological validity of brain research by studying humans moving in real-world settings. Recent work shows that dual-layer EEG can improve the fidelity of electrocortical recordings during gait, but it is unclear whether these positive results extrapolate to non-locomotor paradigms. For our study, we recorded brain activity with dual-layer EEG while participants played table tennis, a whole-body, responsive sport that could help investigate visuomotor feedback, object interception, and performance monitoring. We characterized artifacts with time-frequency analyses and correlated scalp and reference noise data to determine how well different sensors captured artifacts. As expected, individual scalp channels correlated more with noise-matched channel time series than with head and body acceleration. We then compared artifact removal methods with and without the use of the dual-layer noise electrodes. Independent Component Analysis separated channels into components, and we counted the number of high-quality brain components based on the fit of a dipole model and using an automated labeling algorithm. We found that using noise electrodes for data processing provided cleaner brain components. These results advance technological approaches for recording high fidelity brain dynamics in human behaviors requiring whole body movement, which will be useful for brain science research. Full article

(This article belongs to the Special Issue Advances on EEG-Based Sensing and Imaging)

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12 pages, 1591 KiB

Open AccessArticle

Cortical Correlates of Increased Postural Task Difficulty in Young Adults: A Combined Pupillometry and EEG Study

by Melike Kahya, Ke Liao, Kathleen M. Gustafson, Abiodun E. Akinwuntan, Brad Manor and Hannes Devos

Sensors 2022, 22(15), 5594; https://doi.org/10.3390/s22155594 - 26 Jul 2022

Cited by 6 | Viewed by 2019

Abstract

The pupillary response reflects mental effort (or cognitive workload) during cognitive and/or motor tasks including standing postural control. EEG has been shown to be a non-invasive measure to assess the cortical involvement of postural control. The purpose of this study was to understand the effect of increasing postural task difficulty on the pupillary response and EEG outcomes and their relationship in young adults. Fifteen adults completed multiple trials of standing: eyes open, eyes open while performing a dual-task (auditory two-back), eyes occluded, and eyes occluded with a dual-task. Participants stood on a force plate and wore an eye tracker and 256-channel EEG cap during the conditions. The power spectrum was analyzed for absolute theta (4–7 Hz), alpha (8–13 Hz), and beta (13–30 Hz) frequency bands. Increased postural task difficulty was associated with greater pupillary response (p < 0.001) and increased posterior region alpha power (p = 0.001) and fronto-central region theta/beta power ratio (p = 0.01). Greater pupillary response correlated with lower posterior EEG alpha power during eyes-occluded standing with (r = −0.67, p = 0.01) and without (r = −0.69, p = 0.01) dual-task. A greater pupillary response was associated with lower CoP displacement in the anterior–posterior direction during dual-task eyes-occluded standing (r = −0.60, p = 0.04). The pupillary response and EEG alpha power appear to capture similar cortical processes that are increasingly utilized during progressively more challenging postural task conditions. As the pupillary response also correlated with task performance, this measurement may serve as a valuable stand-alone or adjunct tool to understand the underlying neurophysiological mechanisms of postural control. Full article

(This article belongs to the Special Issue Advances on EEG-Based Sensing and Imaging)

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