Fusion Methods for Biosignal Analysis: Theory and Applications
Data modalities include electrocardiography (ECG), electroencephalography (EEG), electrocorticography (ECoG), magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET), and diffusion tensor imaging (DTI). Different combinations of DA with other bioinspired methods (...
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| Published in | Computational Intelligence and Neuroscience Vol. 2017; pp. 1 - 2 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Hindawi Limiteds
01.01.2017
Hindawi John Wiley & Sons, Inc Hindawi Publishing Corporation |
| Series | Fusion Methods for Biosignal Analysis: Theory and Applications |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1687-5265 1687-5273 1687-5273 |
| DOI | 10.1155/2017/7152546 |
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| Abstract | Data modalities include electrocardiography (ECG), electroencephalography (EEG), electrocorticography (ECoG), magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET), and diffusion tensor imaging (DTI). Different combinations of DA with other bioinspired methods (bat algorithm, particle swarm optimization, and enhanced fireworks algorithm) and a neural network-based method called extreme learning machine are implemented. [...]Z. Weng et al. design a multimodal fusion method for localization of hub regions in the brain using MRI T1 and DTI. |
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| AbstractList | Recent advances in data acquisition and biosignal processing are paving the way for the optimal integration or fusion of complementary data modalities in a wide variety of clinical settings. Data modalities include electrocardiography (ECG), electroencephalography (EEG), electrocorticography (ECoG), magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET), and diffusion tensor imaging (DTI). Integration can be performed by exploiting the analyses sequentially or simultaneously, depending on issues related to synchronization, physical compatibilities, and standard clinical procedures. Fusion approaches aim at integrating analyses of data from different modalities, establishing synergic relationships for improved clinical hypothesis testing and medical diagnosis. The heterogeneous nature of data sources from different clinical analyses and acquisition modalities presents big challenges. The main objective of data fusion is to exploit complementary properties of several single-modality methods in order to improve each of them considered separately. In addition, fusion can enable or enhance the approximation to more complex structured results (e.g., hierarchical trees and topological networks). This broad field of research has been named in different ways, for instance, sensor data fusion; decision fusion; multimodal fusion; heterogeneous sensor fusion; mixture of experts; classifier combiners; and multiway signal processing. This special issue focuses on theoretical and application advances in fusion methods for biosignal analysis (FMBA). Fourteen submissions were received and each manuscript was reviewed by at least two external referees. Five papers were finally accepted for the special issue. The accepted papers cover important problems related to computational intelligence and neuroscience. In the paper opening the issue, H. Banville et al. propose a method for mental task evaluation based on the fusion of features extracted from EEG and near-infrared spectroscopy. The tasks evaluated are word generation, mental rotation, subtraction, singing and navigation, and motor and face imagery. The method is intended to improve classification performance for more efficient and flexible brain computer interfaces (BCIs). Y. Huang et al. develop a multimodal late fusion procedure that combines features from facial expression images and EEG data for emotion recognition. A two decision-level fusion is performed to classify four basic emotion states (happiness, neutral, sadness, and fear) and three emotion intensity levels (strong, ordinary, and weak). A general-purpose method for classification named dandelion algorithm (DA) is developed in the paper by X. Li et al. Different combinations of DA with other bioinspired methods (bat algorithm, particle swarm optimization, and enhanced fireworks algorithm) and a neural network-based method called extreme learning machine are implemented. Results show improvement in classification accuracy for different biomedical problems using data from EEG, ECG, and single photon emission computed tomography (SPECT). Data modalities include electrocardiography (ECG), electroencephalography (EEG), electrocorticography (ECoG), magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET), and diffusion tensor imaging (DTI). Different combinations of DA with other bioinspired methods (bat algorithm, particle swarm optimization, and enhanced fireworks algorithm) and a neural network-based method called extreme learning machine are implemented. [...]Z. Weng et al. design a multimodal fusion method for localization of hub regions in the brain using MRI T1 and DTI. |
| Audience | Academic |
| Author | Vicente Zarzoso Luis Vergara Manuel Rosa-Zurera Addisson Salazar |
| AuthorAffiliation | 1 Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain 2 I3S Laboratory, Université Côte d'Azur, CNRS, Sophia Antipolis, France 3 Universidad de Alcalá, Campus Universitario, Alcalá de Henares, Spain |
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| Copyright | Copyright © 2017 Addisson Salazar et al. COPYRIGHT 2017 John Wiley & Sons, Inc. Copyright © 2017 Addisson Salazar et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Distributed under a Creative Commons Attribution 4.0 International License Copyright © 2017 Addisson Salazar et al. 2017 |
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| Snippet | Data modalities include electrocardiography (ECG), electroencephalography (EEG), electrocorticography (ECoG), magnetic resonance imaging (MRI), functional MRI... Recent advances in data acquisition and biosignal processing are paving the way for the optimal integration or fusion of complementary data modalities in a... |
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| SubjectTerms | Algorithms Bioengineering Brain research Computer Science Electrocardiography Electroencephalography Hypothesis testing Life Sciences Localization Machine Learning Medical imaging Methods Neural networks NMR Nuclear magnetic resonance Signal and Image Processing |
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| Title | Fusion Methods for Biosignal Analysis: Theory and Applications |
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