Special Issue "Machine Learning Algorithms for Biomedical Signal Processing"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editors

Dr. Maryam Ravan
Website
Guest Editor
Electrical & Computer Engineering, College of Engineering & Computing Sciences, New York Institute of Technology, New York, NY 10023, USA
Interests: machine learning; biomedical signal/ image processing; microwave holography; radar; nondestructive testing
Dr. Ganesh R. Naik
Website1 Website2
Guest Editor
Marcs Institute For Brain, Behaviour & Development, Western Sydney University, Penrith, NSW 2751, Australia
Interests: Blind source separation; Independent Component Analysis; Biomedical Signal Processing; Human Computer Interaction; Pattern Recognition
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical signal processing aims to provide greater insights into the analysis of the information flows from physiological signals using a variety of mathematical formulae and algorithms.

Many researchers have proposed various novel algorithms and mathematical methods to analyze biomedical signals that significantly pushing the state of the art of biomeasurement tools into a wide range of high-level tasks. Such progress can help to gain a greater perception and to make better decisions in clinical assessments.

The purpose of this Special Issue is to present recent advances in signal processing and machine learning for biomedical signal analysis. We are focusing on original research works in this field, covering new theories, implementations, and mathematical analysis and modeling of time series in living systems and biomedical signals. Potential topics of interest are related to recent advances in machine learning and signal analysis and processing but are not limited to:

  • Biomedical signal processing and analysis;
  • Biomedical image processing and analysis;
  • Neural rehabilitation engineering;
  • Biomedical big data processing;
  • Signal/image processing for human machine interface;
  • Time-frequency and nonstationary biosignal analysis;
  • Machine learning in biomedical applications;
  • Biometrics with biomedical signals;
  • Statistical pattern recognition.

Dr. Maryam Ravan
Dr. Ganesh Naik
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 papers will be 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. Algorithms 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 1000 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.

Published Papers (1 paper)

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Research

Open AccessArticle
An EEG Feature Extraction Method Based on Sparse Dictionary Self-Organizing Map for Event-Related Potential Recognition
Algorithms 2020, 13(10), 259; https://doi.org/10.3390/a13100259 - 13 Oct 2020
Abstract
In the application of the brain-computer interface, feature extraction is an important part of Electroencephalography (EEG) signal classification. Using sparse modeling to extract EEG signal features is a common approach. However, the features extracted by common sparse decomposition methods are only of analytical [...] Read more.
In the application of the brain-computer interface, feature extraction is an important part of Electroencephalography (EEG) signal classification. Using sparse modeling to extract EEG signal features is a common approach. However, the features extracted by common sparse decomposition methods are only of analytical meaning, and cannot relate to actual EEG waveforms, especially event-related potential waveforms. In this article, we propose a feature extraction method based on a self-organizing map of sparse dictionary atoms, which can aggregate event-related potential waveforms scattered inside an over-complete sparse dictionary into the code book of neurons in the self-organizing map network. Then, the cosine similarity between the EEG signal sample and the code vector is used as the classification feature. Compared with traditional feature extraction methods based on sparse decomposition, the classification features obtained by this method have more intuitive electrophysiological meaning. The experiment conducted on a public auditory event-related potential (ERP) brain-computer interface dataset showed that, after the self-organized mapping of dictionary atoms, the neurons’ code vectors in the self-organized mapping network were remarkably similar to the ERP waveform obtained after superposition and averaging. The feature extracted by the proposed method used a smaller amount of data to obtain classification accuracy comparable to the traditional method. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Biomedical Signal Processing)
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