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Brain Sciences
Special Issues
Diagnosis and Prediction of Neurological Diseases: Application of EEG-Based Technology
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Diagnosis and Prediction of Neurological Diseases: Application of EEG-Based Technology

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurotechnology and Neuroimaging".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 7106

Special Issue Editors

Dr. Jesús Pastor

E-Mail Website
Guest Editor
Clinical Neurophysiology, Hospital Universitario de la Princesa, Madrid, Spain
Interests: epilepsy; deep brain stimulation; intraoperative neurophysiological monitoring; quantified electroencephalography; continous electroencephalography monitoring; network theory; multivariate analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Lorena Vega-Zelaya

E-Mail Website
Guest Editor
Clinical Neurophysiology, Biomedical Research Institute Hospital, Universitario de La Princesa, 28006 Madrid, Spain
Interests: neurological diseases; clinical neurology; EEG; neurophysiology; Alzheimer's disease; neuroimaging

Special Issue Information

Dear Colleagues,

Electroencephalography (EEG) will turn one century old next year and can be considered one of the oldest and most consolidated methods to study the brain. However, in recent decades of the twentieth century and during the present, we have seen a renewed concept in the use of EEG, firstly due to the introduction of digitalization, and secondly, due to the massive spreading of numerical methods to unveil brain states and dynamics, which are not easily identified by the naked eye. This set of methods is commonly known as quantified EEG (qEEG).

This Special Issue of Brain Sciences will provide an update on the recent clinical and preclinical advances in the prediction and diagnosis of neurological diseases by means of the numerical methods applied to EEG recordings. We aim to underscore the importance of these recent advances for both clinicians and researchers.

The following topics are subject to particular interest:

  • Utility of qEEG in clinical practice;
  • Novel biomarkers with the potential to improve the classification and risk stratification of dementias and psychiatric pathologies;
  • Usefulness of qEEG in critically ill patients and multimodal neuro-monitoring.

We cordially invite original preclinical, translational, and clinical works as well as review articles regarding the above-mentioned cutting-edge topics for contribution in this Special Issue of Brain Sciences.

Dr. Jesús Pastor
Dr. Lorena Vega-Zelaya
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. Brain Sciences is an international peer-reviewed open access monthly 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 2200 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

  • biomarker
  • coherence
  • multimodal neuro-monitoring
  • qEEG
  • personalized medicine
  • spectral analysis
  • synchronization

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Published Papers (5 papers)

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Research

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28 pages, 9638 KiB  
Article
Structure of Spectral Composition and Synchronization in Human Sleep on the Whole Scalp: A Pilot Study
by Jesús Pastor, Paula Garrido Zabala and Lorena Vega-Zelaya
Brain Sci. 2024, 14(10), 1007; https://doi.org/10.3390/brainsci14101007 - 6 Oct 2024
Viewed by 794
Abstract
We used numerical methods to define the normative structure of the different stages of sleep and wake (W) in a pilot study of 19 participants without pathology (18–64 years old) using a double-banana bipolar montage. Artefact-free 120–240 s epoch lengths were visually identified [...] Read more.
We used numerical methods to define the normative structure of the different stages of sleep and wake (W) in a pilot study of 19 participants without pathology (18–64 years old) using a double-banana bipolar montage. Artefact-free 120–240 s epoch lengths were visually identified and divided into 1 s windows with a 10% overlap. Differential channels were grouped into frontal, parieto-occipital, and temporal lobes. For every channel, the power spectrum (PS) was calculated via fast Fourier transform and used to compute the areas for the delta (0–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands, which were log-transformed. Furthermore, Pearson’s correlation coefficient and coherence by bands were computed. Differences in logPS and synchronization from the whole scalp were observed between the sexes for specific stages. However, these differences vanished when specific lobes were considered. Considering the location and stages, the logPS and synchronization vary highly and specifically in a complex manner. Furthermore, the average spectra for every channel and stage were very well defined, with phase-specific features (e.g., the sigma band during N2 and N3, or the occipital alpha component during wakefulness), although the slow alpha component (8.0–8.5 Hz) persisted during NREM and REM sleep. The average spectra were symmetric between hemispheres. The properties of K-complexes and the sigma band (mainly due to sleep spindles—SSs) were deeply analyzed during the NREM N2 stage. The properties of the sigma band are directly related to the density of SSs. The average frequency of SSs in the frontal lobe was lower than that in the occipital lobe. In approximately 30% of the participants, SSs showed bimodal components in the anterior regions. qEEG can be easily and reliably used to study sleep in healthy participants and patients. Full article
(This article belongs to the Special Issue Diagnosis and Prediction of Neurological Diseases: Application of EEG-Based Technology)
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26 pages, 8077 KiB  
Article
Employing Siamese Networks as Quantitative Biomarker for Assessing the Effect of Dolphin-Assisted Therapy on Pediatric Cerebral Palsy
by Jesús Jaime Moreno Escobar, Oswaldo Morales Matamoros, Erika Yolanda Aguilar del Villar, Hugo Quintana Espinosa and Liliana Chanona Hernández
Brain Sci. 2024, 14(8), 778; https://doi.org/10.3390/brainsci14080778 - 31 Jul 2024
Viewed by 1060
Abstract
This study explores the potential of using a Siamese Network as a biomarker for assessing the effectiveness of Dolphin-Assisted Therapy (DAT) in children with Spastic Cerebral Palsy (SCP). The problem statement revolves around the need for objective measures to evaluate the impact of [...] Read more.
This study explores the potential of using a Siamese Network as a biomarker for assessing the effectiveness of Dolphin-Assisted Therapy (DAT) in children with Spastic Cerebral Palsy (SCP). The problem statement revolves around the need for objective measures to evaluate the impact of DAT on patients with SCP, considering the subjective nature of traditional assessment methods. The methodology involves training a Siamese network, a type of neural network designed to compare similarities between inputs, using data collected from SCP patients undergoing DAT sessions. The study employed Event-Related Potential (ERP) and Fast Fourier Transform (FFT) analyses to examine cerebral activity and brain rhythms, proposing the use of SNN to compare electroencephalographic (EEG) signals of children with cerebral palsy before and after Dolphin-Assisted Therapy. Testing on samples from four children yielded a high average similarity index of 0.9150, indicating consistent similarity metrics before and after therapy. The network is trained to learn patterns and similarities between pre- and post-therapy evaluations, in order to identify biomarkers indicative of therapy effectiveness. Notably, the Siamese Network’s architecture ensures that comparisons are made within the same feature space, allowing for more accurate assessments. The results of the study demonstrate promising findings, indicating different patterns in the output of the Siamese Network that correlate with improvements in symptoms of SCP post-DAT. Confirming these observations will require large, longitudinal studies but such findings would suggest that the Siamese Network could have utility as a biomarker in monitoring treatment responses for children with SCP who undergo DAT and offer them more objective as well as quantifiable manners of assessing therapeutic interventions. Great discrepancies in neuronal voltage perturbations, 7.9825 dB on average at the specific samples compared to the whole dataset (6.2838 dB), imply a noted deviation from resting activity. These findings indicate that Dolphin-Assisted Therapy activates particular brain regions specifically during the intervention. Full article
(This article belongs to the Special Issue Diagnosis and Prediction of Neurological Diseases: Application of EEG-Based Technology)
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20 pages, 5668 KiB  
Article
Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia
by Wang Wan, Zhongze Gu, Chung-Kang Peng and Xingran Cui
Brain Sci. 2024, 14(5), 487; https://doi.org/10.3390/brainsci14050487 - 11 May 2024
Viewed by 1709
Abstract
Exploring the spatiotemporal dynamic patterns of multi-channel electroencephalography (EEG) is crucial for interpreting dementia and related cognitive decline. Spatiotemporal patterns of EEG can be described through microstate analysis, which provides a discrete approximation of the continuous electric field patterns generated by the brain [...] Read more.
Exploring the spatiotemporal dynamic patterns of multi-channel electroencephalography (EEG) is crucial for interpreting dementia and related cognitive decline. Spatiotemporal patterns of EEG can be described through microstate analysis, which provides a discrete approximation of the continuous electric field patterns generated by the brain cortex. Here, we propose a novel microstate spatiotemporal dynamic indicator, termed the microstate sequence non-randomness index (MSNRI). The essence of the method lies in initially generating a sequence of microstate transition patterns through state space compression of EEG data using microstate analysis. Following this, we assess the non-randomness of these microstate patterns using information-based similarity analysis. The results suggest that this MSNRI metric is a potential marker for distinguishing between health control (HC) and frontotemporal dementia (FTD) (HC vs. FTD: 6.958 vs. 5.756, p < 0.01), as well as between HC and populations with Alzheimer’s disease (AD) (HC vs. AD: 6.958 vs. 5.462, p < 0.001). Healthy individuals exhibit more complex macroscopic structures and non-random spatiotemporal patterns of microstates, whereas dementia disorders lead to more random spatiotemporal patterns. Additionally, we extend the proposed method by integrating the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method to explore spatiotemporal dynamic patterns of microstates at specific frequency scales. Moreover, we assessed the effectiveness of this innovative method in predicting cognitive scores. The results demonstrate that the incorporation of CEEMD-enhanced microstate dynamic indicators significantly improved the prediction accuracy of Mini-Mental State Examination (MMSE) scores (R2 = 0.940). The CEEMD-enhanced MSNRI method not only aids in the exploration of large-scale neural changes in populations with dementia but also offers a robust tool for characterizing the dynamics of EEG microstate transitions and their impact on cognitive function. Full article
(This article belongs to the Special Issue Diagnosis and Prediction of Neurological Diseases: Application of EEG-Based Technology)
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18 pages, 1646 KiB  
Article
Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children
by Min Feng and Juncai Xu
Brain Sci. 2024, 14(5), 469; https://doi.org/10.3390/brainsci14050469 - 7 May 2024
Viewed by 1453
Abstract
Attention deficit hyperactivity disorder (ADHD) is a neuro-developmental disorder that affects approximately 5–10% of school-aged children worldwide. Early diagnosis and intervention are essential to improve the quality of life of patients and their families. In this study, we propose ConvMixer-ECA, a novel deep [...] Read more.
Attention deficit hyperactivity disorder (ADHD) is a neuro-developmental disorder that affects approximately 5–10% of school-aged children worldwide. Early diagnosis and intervention are essential to improve the quality of life of patients and their families. In this study, we propose ConvMixer-ECA, a novel deep learning architecture that combines ConvMixer with efficient channel attention (ECA) blocks for the accurate diagnosis of ADHD using electroencephalogram (EEG) signals. The model was trained and evaluated using EEG recordings from 60 healthy children and 61 children with ADHD. A series of experiments were conducted to evaluate the performance of the ConvMixer-ECA. The results showed that the ConvMixer-ECA performed well in ADHD recognition with 94.52% accuracy. The incorporation of attentional mechanisms, in particular ECA, improved the performance of ConvMixer; it outperformed other attention-based variants. In addition, ConvMixer-ECA outperformed state-of-the-art deep learning models including EEGNet, CNN, RNN, LSTM, and GRU. t-SNE visualization of the output of this model layer validated the effectiveness of ConvMixer-ECA in capturing the underlying patterns and features that separate ADHD from typically developing individuals through hierarchical feature learning. These outcomes demonstrate the potential of ConvMixer-ECA as a valuable tool to assist clinicians in the early diagnosis and intervention of ADHD in children. Full article
(This article belongs to the Special Issue Diagnosis and Prediction of Neurological Diseases: Application of EEG-Based Technology)
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Review

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24 pages, 18899 KiB  
Review
Utility of Quantitative EEG in Neurological Emergencies and ICU Clinical Practice
by Misericordia Veciana de las Heras, Jacint Sala-Padro, Jordi Pedro-Perez, Beliu García-Parra, Guillermo Hernández-Pérez and Merce Falip
Brain Sci. 2024, 14(9), 939; https://doi.org/10.3390/brainsci14090939 - 20 Sep 2024
Viewed by 1324
Abstract
The electroencephalogram (EEG) is a cornerstone tool for the diagnosis, management, and prognosis of selected patient populations. EEGs offer significant advantages such as high temporal resolution, real-time cortical function assessment, and bedside usability. The quantitative EEG (qEEG) added the possibility of long recordings [...] Read more.
The electroencephalogram (EEG) is a cornerstone tool for the diagnosis, management, and prognosis of selected patient populations. EEGs offer significant advantages such as high temporal resolution, real-time cortical function assessment, and bedside usability. The quantitative EEG (qEEG) added the possibility of long recordings being processed in a compressive manner, making EEG revision more efficient for experienced users, and more friendly for new ones. Recent advancements in commercially available software, such as Persyst, have significantly expanded and facilitated the use of qEEGs, marking the beginning of a new era in its application. As a result, there has been a notable increase in the practical, real-world utilization of qEEGs in recent years. This paper aims to provide an overview of the current applications of qEEGs in daily neurological emergencies and ICU practice, and some elementary principles of qEEGs using Persyst software in clinical settings. This article illustrates basic qEEG patterns encountered in critical care and adopts the new terminology proposed for spectrogram reporting. Full article
(This article belongs to the Special Issue Diagnosis and Prediction of Neurological Diseases: Application of EEG-Based Technology)
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