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Special Issue "Advanced Machine Learning and Deep Networks for Psycho-Physiological Signals Processing, Modelling, and Classification"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 August 2019

Special Issue Editor

Guest Editor
Prof. Dr. George Magoulas

Department of Computer Science and Information Systems, Birkbeck College, University of London, London WC1E 7HX, UK
Website | E-Mail
Interests: intelligent optimization; context awareness; neural computing; deep learning; data-driven modelling; intelligent systems; machine learning; nature-inspired computing; user modelling; computational models of learning and cognition

Special Issue Information

Dear Colleagues,

Psycho-physiological signals have been demonstrated as being useful in several applications for assessing emotional experiences, modelling cognitive processes, user and player modelling, human activity recognition, classification of facial expressions, detection of behavioural changes and so on. Signals come from a wide range of sensors, such as wearable sensors, mobile sensors, cameras, heart rate monitoring devices, EEG headcaps and headbands, ECG sensors, breathing monitors, EMG sensors, and temperature sensors. However, the use of these signals poses several challenges for reliable data processing, modelling and classification, as it is influenced by different types of environmental and biological sources of noise, artefacts and interference. Methods that employ machine learning and deep learning appear eminently suitable for these challenging tasks.

This Special Issue will present state-of-the-art machine learning and deep learning approaches for data processing, modelling, pattern recognition, and the classification of psycho-physiological signals, and for the development of intelligent systems that use psycho-physiological signals.

Prof. Dr. George Magoulas
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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

(1) Processing of psycho-physiological signals and time-series: 
  • Data transformation 
  • Dimensionality reduction 
  • Feature selection 
  • Filtering

(2) Modelling, classification and pattern recognition methods: 

  • Bio-inspired computing 
  • Clustering 
  • Decision trees 
  • Deep networks 
  • Ensemble learning 
  • Fuzzy logic 
  • Genetic and evolution algorithms 
  • Kernel methods 
  • Machine learning 
  • Neural networks 
  • Random forests 
  • Support vectors
  • Tensors

(3) Visualisation of psycho-physiological signals and time-series

(4) Intelligent systems and human-machine systems that use psycho-physiological signals

Published Papers (5 papers)

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Research

Open AccessArticle
A Novel Eye Movement Data Transformation Technique that Preserves Temporal Information: A Demonstration in a Face Processing Task
Sensors 2019, 19(10), 2377; https://doi.org/10.3390/s19102377
Received: 16 April 2019 / Revised: 7 May 2019 / Accepted: 22 May 2019 / Published: 23 May 2019
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Abstract
Existing research has shown that human eye-movement data conveys rich information about underlying mental processes, and that the latter may be inferred from the former. However, most related studies rely on spatial information about which different areas of visual stimuli were looked at, [...] Read more.
Existing research has shown that human eye-movement data conveys rich information about underlying mental processes, and that the latter may be inferred from the former. However, most related studies rely on spatial information about which different areas of visual stimuli were looked at, without considering the order in which this occurred. Although powerful algorithms for making pairwise comparisons between eye-movement sequences (scanpaths) exist, the problem is how to compare two groups of scanpaths, e.g., those registered with vs. without an experimental manipulation in place, rather than individual scanpaths. Here, we propose that the problem might be solved by projecting a scanpath similarity matrix, obtained via a pairwise comparison algorithm, to a lower-dimensional space (the comparison and dimensionality-reduction techniques we use are ScanMatch and t-SNE). The resulting distributions of low-dimensional vectors representing individual scanpaths can be statistically compared. To assess if the differences result from temporal scanpath features, we propose to statistically compare the cross-validated accuracies of two classifiers predicting group membership: (1) based exclusively on spatial metrics; (2) based additionally on the obtained scanpath representation vectors. To illustrate, we compare autistic vs. typically-developing individuals looking at human faces during a lab experiment and find significant differences in temporal scanpath features. Full article
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Open AccessArticle
A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks
Sensors 2019, 19(9), 1992; https://doi.org/10.3390/s19091992
Received: 7 March 2019 / Revised: 23 April 2019 / Accepted: 24 April 2019 / Published: 28 April 2019
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Abstract
Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neural network [...] Read more.
Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neural network (CNN) in feature extraction, the power of discrete wavelet transform (DWT) in signal processing, and the power of long short-term memory (LSTM) in signal classification. A CNN–DWT–LSTM method is proposed to classify the computed tomography (CT) images of livers with tumors and to classify the magnetic resonance (MR) images of brains with tumors. The proposed method classifies liver tumors images as benign or malignant and then classifies brain tumor images as meningioma, glioma, and pituitary. In the hybrid CNN–DWT–LSTM method, the feature vector of the images is obtained from pre-trained AlexNet CNN architecture. The feature vector is reduced but strengthened by applying the single-level one-dimensional discrete wavelet transform (1-D DWT), and it is classified by training with an LSTM network. Under the scope of the study, images of 56 benign and 56 malignant liver tumors that were obtained from Fırat University Research Hospital were used and a publicly available brain tumor dataset were used. The experimental results show that the proposed method had higher performance than classifiers, such as K-nearest neighbors (KNN) and support vector machine (SVM). By using the CNN–DWT–LSTM hybrid method, an accuracy rate of 99.1% was achieved in the liver tumor classification and accuracy rate of 98.6% was achieved in the brain tumor classification. We used two different datasets to demonstrate the performance of the proposed method. Performance measurements show that the proposed method has a satisfactory accuracy rate at the liver tumor and brain tumor classifying. Full article
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Open AccessArticle
Open Database for Accurate Upper-Limb Intent Detection Using Electromyography and Reliable Extreme Learning Machines
Sensors 2019, 19(8), 1864; https://doi.org/10.3390/s19081864
Received: 15 March 2019 / Revised: 12 April 2019 / Accepted: 14 April 2019 / Published: 18 April 2019
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Abstract
Surface Electromyography (sEMG) signal processing has a disruptive technology potential to enable a natural human interface with artificial limbs and assistive devices. However, this biosignal real-time control interface still presents several restrictions such as control limitations due to a lack of reliable signal [...] Read more.
Surface Electromyography (sEMG) signal processing has a disruptive technology potential to enable a natural human interface with artificial limbs and assistive devices. However, this biosignal real-time control interface still presents several restrictions such as control limitations due to a lack of reliable signal prediction and standards for signal processing among research groups. Our paper aims to present and validate our sEMG database through the signal classification performed by the reliable forms of our Extreme Learning Machines (ELM) classifiers, used to maintain a more consistent signal classification. To perform the signal processing, we explore the use of a stochastic filter based on the Antonyan Vardan Transform (AVT) in combination with two variations of our Reliable classifiers (denoted R-ELM and R-Regularized ELM (RELM), respectively), to derive a reliability metric from the system, which autonomously selects the most reliable samples for the signal classification. To validate and compare our database and classifiers with related papers, we performed the classification of the whole of Databases 1, 2, and 6 (DB1, DB2, and DB6) of the NINAProdatabase. Our database presented consistent results, while the reliable forms of ELM classifiers matched or outperformed related papers, reaching average accuracies higher than 99 % for the IEEdatabase, while average accuracies of 75.1 %, 79.77 %, and 69.83 % were achieved for NINAPro DB1, DB2, and DB6, respectively. Full article
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Open AccessArticle
Myocardium Detection by Deep SSAE Feature and Within-Class Neighborhood Preserved Support Vector Classifier and Regressor
Sensors 2019, 19(8), 1766; https://doi.org/10.3390/s19081766
Received: 1 March 2019 / Revised: 16 March 2019 / Accepted: 28 March 2019 / Published: 13 April 2019
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Abstract
Automatic detection of left ventricle myocardium is essential to subsequent cardiac image registration and tissue segmentation. However, it is considered challenging mainly because of the complex and varying shape of the myocardium and surrounding tissues across slices and phases. In this study, a [...] Read more.
Automatic detection of left ventricle myocardium is essential to subsequent cardiac image registration and tissue segmentation. However, it is considered challenging mainly because of the complex and varying shape of the myocardium and surrounding tissues across slices and phases. In this study, a hybrid model is proposed to detect myocardium in cardiac magnetic resonance (MR) images combining region proposal and deep feature classification and regression. The model firstly generates candidate regions using new structural similarity-enhanced supervoxel over-segmentation plus hierarchical clustering. Then it adopts a deep stacked sparse autoencoder (SSAE) network to learn the discriminative deep feature to represent the regions. Finally, the features are fed to train a novel nonlinear within-class neighborhood preserved soft margin support vector (C-SVC) classifier and multiple-output support vector ( ε -SVR) regressor for refining the location of myocardium. To improve the stability and generalization, the model also takes hard negative sample mining strategy to fine-tune the SSAE and the classifier. The proposed model with impacts of different components were extensively evaluated and compared to related methods on public cardiac data set. Experimental results verified the effectiveness of proposed integrated components, and demonstrated that it was robust in myocardium localization and outperformed the state-of-the-art methods in terms of typical metrics. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement. Full article
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Open AccessArticle
Predicting Depth from Single RGB Images with Pyramidal Three-Streamed Networks
Sensors 2019, 19(3), 667; https://doi.org/10.3390/s19030667
Received: 2 January 2019 / Revised: 1 February 2019 / Accepted: 2 February 2019 / Published: 6 February 2019
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Abstract
Predicting depth from a monocular image is an ill-posed and inherently ambiguous issue in computer vision. In this paper, we propose a pyramidal third-streamed network (PTSN) that recovers the depth information using a single given RGB image. PTSN uses pyramidal structure images, which [...] Read more.
Predicting depth from a monocular image is an ill-posed and inherently ambiguous issue in computer vision. In this paper, we propose a pyramidal third-streamed network (PTSN) that recovers the depth information using a single given RGB image. PTSN uses pyramidal structure images, which can extract multiresolution features to improve the robustness of the network as the network input. The full connection layer is changed into fully convolutional layers with a new upconvolution structure, which reduces the network parameters and computational complexity. We propose a new loss function including scale-invariant, horizontal and vertical gradient loss that not only helps predict the depth values, but also clearly obtains local contours. We evaluate PTSN on the NYU Depth v2 dataset and the experimental results show that our depth predictions have better accuracy than competing methods. Full article
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