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: 31 May 2024 | Viewed by 1578

Special Issue Editors


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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

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

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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. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

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

Published Papers (2 papers)

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Research

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
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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
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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 627
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
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