Advanced Technologies and Applications of Brain Sciences

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 894

Special Issue Editor


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Guest Editor
Division of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia
Interests: cognitive neurology; pain

Special Issue Information

Dear Colleagues,

We invite you to contribute to the Special Issue "Advanced Technologies and Applications of Brain Sciences" of Applied Sciences.

We aim to bridge the gap between cutting-edge technology, brain sciences, clinical neurology, psychiatry, and neurorehabilitation. With advanced technologies, we can diagnose and improve activities in the brain network. We can also use virtual reality and haptic devices, as well as facilitate neurorehabilitation.

This interdisciplinary Special Issue will focus on original research articles and reviews, advancing our understanding of the neurophysiology of brain networks and pathological processes of neurological and psychiatric disorders. The articles will cover the consequences of neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease, stroke, neurotrauma, depression, and schizophrenia.

We encourage submissions related to both healthy populations and patients, with a focus on areas such as electroencephalography (EEG), transcranial magnetic stimulation (TMS), haptic devices, virtual reality (VR), deep brain stimulation (DBS), and computer–brain interfaces. We also welcome works related to the software used for analysis and device support.

As we push the boundaries of technology and brain sciences, we hope to inspire further research to improve brain health and well-being.

We are looking forward to your submissions.

Dr. Martin Rakuša
Guest Editor

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. Applied Sciences 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 2400 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

  • electroencephalography (EEG)
  • transcranial magnetic stimulation (TMS)
  • haptic devices
  • virtual reality (VR)
  • deep brain stimulation (DBS)
  • computer–brain interfaces
  • software
  • neurodegenerative disorders
  • dementia
  • mild cognitive impairment
  • Alzheimer's disease
  • Parkinson's disease
  • stroke
  • neurotrauma
  • depression
  • schizophrenia

Published Papers (1 paper)

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Research

23 pages, 8091 KiB  
Article
Bridging Modalities: A Multimodal Machine Learning Approach for Parkinson’s Disease Diagnosis Using EEG and MRI Data
by Manal Alrawis, Saad Al-Ahmadi and Farah Mohammad
Appl. Sci. 2024, 14(9), 3883; https://doi.org/10.3390/app14093883 - 1 May 2024
Viewed by 481
Abstract
Parkinson’s disease (PD) is a slowly progressing neurological disorder with symptoms that overlap with those of other conditions, making early detection and accurate diagnosis vital for effective treatment and a patient’s quality of life. Symptoms such as tremors, stiffness, slow movements, and balance [...] Read more.
Parkinson’s disease (PD) is a slowly progressing neurological disorder with symptoms that overlap with those of other conditions, making early detection and accurate diagnosis vital for effective treatment and a patient’s quality of life. Symptoms such as tremors, stiffness, slow movements, and balance issues, along with psychiatric manifestations, are typical of PD. This study introduces a groundbreaking approach to PD diagnosis, utilizing a multimodal machine learning framework that integrates Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data. Focusing on the early detection and accurate classification of PD, the proposed research leverages the distinct yet complementary nature of EEG and MRI datasets to enhance diagnostic precision. We employed a robust algorithmic strategy, including LightGBM and machine learning techniques, to analyze the complex patterns inherent in neurological data. The key steps of the proposed research are preprocessing and feature extraction from both EEG and MRI modalities, followed by their fusion using Principal Component Analysis (PCA) for dimensionality reduction. The fused dataset was then analyzed using a LightGBM model and validated through a 10-fold cross-validation process to ensure reliability and stability. The model’s efficacy was further tested on independent datasets, demonstrating its robustness across diverse patient demographics. The obtained results showcased an accuracy of 97.17%, sensitivity of 96.58%, and specificity of 96.82% in PD classification, outperforming traditional multimodal as well as single-modality diagnostic methods. The integration of EEG and MRI data provided a more comprehensive view of the neurophysiological and neuroanatomical changes associated with PD. Additionally, the use of advanced machine learning algorithms allowed for a nuanced analysis, capturing subtle patterns indicative of early PD stages. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications of Brain Sciences)
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