Special Issue "Entropy on Biosignals and Intelligent Systems II"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editor

Prof. Dr. Carlos M. Travieso-González
E-Mail Website
Guest Editor
Signals and Communications Department, Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), Campus Universitario de Tafira, sn. Telecomunicación. Pabellón B. D-111. E-35017. Las Palmas de Gran Canaria, Spain
Interests: biometrics; biomedical signals; data mining
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Special Issue Information

Dear Colleagues,

Many specifics of biosignals and intelligent systems are not well addressed by the conventional models currently used in the field of artificial intelligence. The purpose of the Special Issue on “Entropy on Intelligent Systems for Biosignals II” is to present and discuss novel trends, ideas, works, and results related to alternative techniques for bioinspired approaches, which show new perspectives.

At present, studies based on advanced and complex systems have opened new doors in research topics and, in particular, to improve the quality and the results on different applications. Biosignals and intelligent systems easily take care of this task and are also useful in areas such as biodiversity conservation, biomedicine, security applications, etc.

This Special Issue focuses on original and new research results concerning bioinspired systems in science and engineering. Manuscripts discussing biosignals and intelligent systems, and their entropy on applications, are welcome; additionally, submissions addressing novel issues, as well as those addressing more specific topics that illustrate the broad impact of bioinspired entropy-based techniques on image coding, processing and analysis, machine and deep learning approaches, signal processing and analysis, natural sounds, and video analysis, are welcome, although the Special Issue is not limited to them.

Prof. Dr. Carlos Travieso-González
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. Entropy 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 1600 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

  • Biosignal entropy
  • Pattern recognition
  • Entropy in natural environments
  • Artificial intelligence techniques
  • Biomedical engineering
  • Bioacoustics
  • Machine and deep learning for biosignals
  • Data mining
  • Biomathemathics
  • Biostatistic

Published Papers (2 papers)

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Research

Open AccessArticle
Conditional Adversarial Domain Adaptation Neural Network for Motor Imagery EEG Decoding
Entropy 2020, 22(1), 96; https://doi.org/10.3390/e22010096 - 13 Jan 2020
Abstract
Decoding motor imagery (MI) electroencephalogram (EEG) signals for brain-computer interfaces (BCIs) is a challenging task because of the severe non-stationarity of perceptual decision processes. Recently, deep learning techniques have had great success in EEG decoding because of their prominent ability to learn features [...] Read more.
Decoding motor imagery (MI) electroencephalogram (EEG) signals for brain-computer interfaces (BCIs) is a challenging task because of the severe non-stationarity of perceptual decision processes. Recently, deep learning techniques have had great success in EEG decoding because of their prominent ability to learn features from raw EEG signals automatically. However, the challenge that the deep learning method faces is that the shortage of labeled EEG signals and EEGs sampled from other subjects cannot be used directly to train a convolutional neural network (ConvNet) for a target subject. To solve this problem, in this paper, we present a novel conditional domain adaptation neural network (CDAN) framework for MI EEG signal decoding. Specifically, in the CDAN, a densely connected ConvNet is firstly applied to obtain high-level discriminative features from raw EEG time series. Then, a novel conditional domain discriminator is introduced to work as an adversarial with the label classifier to learn commonly shared intra-subjects EEG features. As a result, the CDAN model trained with sufficient EEG signals from other subjects can be used to classify the signals from the target subject efficiently. Competitive experimental results on a public EEG dataset (High Gamma Dataset) against the state-of-the-art methods demonstrate the efficacy of the proposed framework in recognizing MI EEG signals, indicating its effectiveness in automatic perceptual decision decoding. Full article
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems II)
Open AccessArticle
Information-Theoretical Criteria for Characterizing the Earliness of Time-Series Data
Entropy 2020, 22(1), 49; https://doi.org/10.3390/e22010049 - 30 Dec 2019
Abstract
Biomedical signals constitute time-series that sustain machine learning techniques to achieve classification. These signals are complex with measurements of several features over, eventually, an extended period. Characterizing whether the data can anticipate prediction is an essential task in time-series mining. The ability to [...] Read more.
Biomedical signals constitute time-series that sustain machine learning techniques to achieve classification. These signals are complex with measurements of several features over, eventually, an extended period. Characterizing whether the data can anticipate prediction is an essential task in time-series mining. The ability to obtain information in advance by having early knowledge about a specific event may be of great utility in many areas. Early classification arises as an extension of the time-series classification problem, given the need to obtain a reliable prediction as soon as possible. In this work, we propose an information-theoretic method, named Multivariate Correlations for Early Classification (MCEC), to characterize the early classification opportunity of a time-series. Experimental validation is performed on synthetic and benchmark data, confirming the ability of the MCEC algorithm to perform a trade-off between accuracy and earliness in a wide-spectrum of time-series data, such as those collected from sensors, images, spectrographs, and electrocardiograms. Full article
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems II)
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