Special Issue "Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing"

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

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editor

Dr. Hirokazu Doi
E-Mail Website
Guest Editor
Graduate School of Engineering, Kokushikan University, 154-8515 Tokyo, Japan
Interests: social perception; emotion; attractiveness computing; EEG/ERP; digital phenotyping

Special Issue Information

Dear Colleagues,

Bioelectric signals contain a vast amount of information, and researchers in diverse fields including cognitive neuroscience, psychiatry, and affective engineering have utilized features extracted from bioelectric signals as a reliable and objective measure of human and animal physiological activation. The introduction of easy-to-use libraries of machine learning has made various kinds of machine learning algorithms accessible to researchers outside the engineering and data-science fields. Consequently, the application of machine learning has enabled researchers to gain novel insight into physiological functions and utilize bioelectric information that has hitherto been missed or neglected by traditional methods of signal processing.

This Special Issue on “Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing” aims to provide a platform to exchange information on the state of the art of bioelectric signal processing using machine learning techniques. Researchers are invited to submit original research articles and review articles relevant to this theme. Articles on application of machine learning in adjacent areas of research such as optical imaging of neural activation, e.g., near-infrared spectroscopy, and non-contact measurement of physiological responses are also welcome. Potential topics include but are not limited to the following:

  • Novel machine learning algorithm for bioelectricity data processing;
  • Application of machine learning in real-time processing of bioelectric signals;
  • Analysis of central and peripheral nervous system activation by machine learning;
  • Automatic classification of people with/without pathological conditions;
  • BCI/BMI.
Dr. Hirokazu Doi
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. 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 2000 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

  • machine learning
  • EEG/ERP
  • ECG
  • BMI/BCI

Published Papers

This special issue is now open for submission.
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