scholarly journals Mental Workload Detection Based on EEG Analysis

2021 ◽  
Author(s):  
José Yauri ◽  
Aura Hernández-Sabaté ◽  
Paul Folch ◽  
Débora Gil
Keyword(s):  
Mental Workload ◽  
Mental States ◽  
Health Conditions ◽  
Eeg Analysis ◽  
Proposed Model ◽  
Binary Neural Network ◽  
High Level ◽  
Leave One Out ◽  
Electroencephalogram Eeg ◽  
Well Models

The study of mental workload becomes essential for human work efficiency, health conditions and to avoid accidents, since workload compromises both performance and awareness. Although workload has been widely studied using several physiological measures, minimising the sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown a high correlation to specific cognitive and mental states like workload. However, there is not enough evidence in the literature to validate how well models generalize in case of new subjects performing tasks of a workload similar to the ones included during model’s training. In this paper we propose a binary neural network to classify EEG features across different mental workloads. Two workloads, low and medium, are induced using two variants of the N-Back Test. The proposed model was validated in a dataset collected from 16 subjects and shown a high level of generalization capability: model reported an average recall of 81.81% in a leave-one-out subject evaluation.

scholarly journals Understanding of Emotional States in Persons with Pedophilia / Pedophilic Disorder

2018 ◽  
Vol 7 (3) ◽  
pp. 84-99
Author(s):  
L.Y. Demidova ◽  
N.V. Dvoryanchikov
Keyword(s):  
Emotional Regulation ◽  
Mental States ◽  
Control Group ◽  
Emotional States ◽  
Sexual Crimes ◽  
Sexual Drive ◽  
Emotional Perception ◽  
Wide Range ◽  
Icd 10 ◽  
High Level

This article highlights the problem of emotional perception in pedophilia (ICD-10) / pedophilia disorder (ICD-11). In present paper, emotional perception is considered as abilities of recognizing and identifying a wide range of mental states like emotions, affects, moods, feelings. The assumption about relations of alexithymia and disturbances in the recognition of emotions, perspective taking, empathy with pedophilia and regulatory mechanisms of activity verified empirically. Two groups of persons accused of sexual crimes are compared: 44 people with pedophilia, 32 people without the disorder; also 95 persons who haven't been accused were examined for the control group; as well intra-group comparison of pedophilic persons with egosyntonic and egodystonic attitude toward sexual drive was made. Contradictions of earlier studies are resolved in the result: it is shown that in pedophilia the ability of understanding emotional states remains normal at first sight (in comparison with the deficits found in the accused without pedophilia). However, the group with pedophilia is characterized by extremely high level of alexithymia and based on this the consistently conclusion is made about disturbances of emotional regulation in egosyntonic form of this disorder.

scholarly journals IntroVNMT: An Introspective Model for Variational Neural Machine Translation

2020 ◽  
Vol 34 (05) ◽  
pp. 8830-8837
Author(s):  
Xin Sheng ◽  
Linli Xu ◽  
Junliang Guo ◽  
Jingchang Liu ◽  
Ruoyu Zhao ◽  
...  
Keyword(s):  
Machine Translation ◽  
Latent Variables ◽  
Image Synthesis ◽  
Target Language ◽  
Generative Adversarial Network ◽  
Neural Machine Translation ◽  
Adversarial Network ◽  
Proposed Model ◽  
Model Training ◽  
High Level

We propose a novel introspective model for variational neural machine translation (IntroVNMT) in this paper, inspired by the recent successful application of introspective variational autoencoder (IntroVAE) in high quality image synthesis. Different from the vanilla variational NMT model, IntroVNMT is capable of improving itself introspectively by evaluating the quality of the generated target sentences according to the high-level latent variables of the real and generated target sentences. As a consequence of introspective training, the proposed model is able to discriminate between the generated and real sentences of the target language via the latent variables generated by the encoder of the model. In this way, IntroVNMT is able to generate more realistic target sentences in practice. In the meantime, IntroVNMT inherits the advantages of the variational autoencoders (VAEs), and the model training process is more stable than the generative adversarial network (GAN) based models. Experimental results on different translation tasks demonstrate that the proposed model can achieve significant improvements over the vanilla variational NMT model.

scholarly journals Simplified and Accurate Stiffness of a Prismatic Anisotropic Thin-Walled Box

2018 ◽  
Vol 12 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Giacomo Canale ◽  
Felice Rubino ◽  
Paul M. Weaver ◽  
Roberto Citarella ◽  
Angelo Maligno
Keyword(s):  
Cross Sections ◽  
Torsional Stiffness ◽  
Element Analysis ◽  
Cross Sectional ◽  
Analysis And Design ◽  
Proposed Model ◽  
Vertical Walls ◽  
Thin Walled ◽  
High Level ◽  
Realistic Geometries

Background:Beam models have been proven effective in the preliminary analysis and design of aerospace structures. Accurate cross sectional stiffness constants are however needed, especially when dealing with bending, torsion and bend-twist coupling deformations. Several models have been proposed in the literature, even recently, but a lack of precision may be found when dealing with a high level of anisotropy and different lay-ups.Objective:A simplified analytical model is proposed to evaluate bending and torsional stiffness of a prismatic, anisotropic, thin-walled box. The proposed model is an extension of the model proposed by Lemanski and Weaver for the evaluation of the bend-twist coupling constant.Methods:Bending and torsional stiffness are derived analytically by using physical reasoning and by applying bending and torsional stiffness mathematic definition. Unitary deformations have been applied when evaluation forces and moments arising on the cross section.Results:Good accuracy has been obtained for structures with different geometries and lay-ups. The model has been validated with respect to finite element analysis. Numerical results are commented upon and compared with other models presented in literature.Conclusion:For cross sections with a high level of anisotropy, the accuracy of the proposed formulation is within 2% for bending stiffness and 6% for torsional stiffness. The percentage of error is further reduced for more realistic geometries and lay-ups.The proposed formulation gives accurate results for different dimensions and length rations of horizontal and vertical walls.

A Model of Metabolic Supply-Demand Mismatch Leading to Secondary Brain Injury

2021 ◽  
Author(s):  
Jiangling Song ◽  
Jennifer A. Kim ◽  
Aaron Frank Struck ◽  
Rui Zhang ◽  
M. Brandon Westover
Keyword(s):  
Brain Injury ◽  
Mechanistic Model ◽  
Acute Brain Injury ◽  
Extracellular Potassium ◽  
Secondary Brain Injury ◽  
Proposed Model ◽  
The Common ◽  
High Level ◽  
The Brain ◽  
Common Association

Secondary brain injury (SBI) is defined as new or worsening injury to the brain after an initial neurologic insult, such as hemorrhage, trauma, ischemic stroke, or infection. It is a common and potentially preventable complication following many types of primary brain injury (PBI). However, mechanistic details about how PBI leads to additional brain injury and evolves into SBI are poorly characterized. In this work, we propose a mechanistic model for the metabolic supply demand mismatch hypothesis (MSDMH) of SBI. Our model, based on the Hodgkin-Huxley model, supplemented with additional dynamics for extracellular potassium, oxygen concentration and excitotoxity, provides a high-level unified explanation for why patients with acute brain injury frequently develop SBI. We investigate how decreased oxygen, increased extracellular potassium, excitotoxicity, and seizures can induce SBI, and suggest three underlying paths for how events following PBI may lead to SBI. The proposed model also helps explain several important empirical observations, including the common association of acute brain injury with seizures, the association of seizures with tissue hypoxia and so on. In contrast to current practices which assume that ischemia plays the predominant role in SBI, our model suggests that metabolic crisis involved in SBI can also be non-ischemic. Our findings offer a more comprehensive understanding of the complex interrelationship among potassium, oxygen, excitotoxicity, seizures and SBI.

Pengamanan Data melalui Model Super Enkripsi Autokey Cipher dan Transposisi Kolom

2021 ◽  
Vol 5 (6) ◽  
pp. 1113-1119
Author(s):  
Muhammad Fadlan ◽  
Haryansyah ◽  
Rosmini
Keyword(s):  
Data Security ◽  
Measurement Tool ◽  
Test Results ◽  
Avalanche Effect ◽  
A Value ◽  
Proposed Model ◽  
High Level ◽  
Cryptographic Techniques ◽  
Effect Test ◽  
Effect Method

One of the essential instruments in the cyber era is data. Therefore, maintaining data security is an important thing to do. One way that can be done to maintain data security is through cryptography. In cryptography, two basic techniques are commonly used, namely substitution techniques and transposition techniques. One of the weaknesses of the basic cryptographic techniques is the lower level of data security. This study proposed a super encryption model in securing data by combining cryptographic algorithms with substitution techniques, i.e., autokey cipher and transposition, i.e., columnar transposition cipher. This study used the Avalanche Effect method as a measurement tool for the proposed super encryption model. The test results have shown that the proposed super encryption model can provide a better level of security. The avalanche effect test on the five data test shows that the average AE value of the proposed super encryption model is 30.76%. This value is higher than the single autokey cipher algorithm of 1.66% and column transposition with a value of 18.03%. Other results from the five data test have shown that the proposed model has a high level of accuracy of 100% in terms of the decryption process results, which is the same as the initial data before going through the encryption process.  

A Novel Neural Model with Lateral Interaction for Learning Tasks

2020 ◽  
pp. 1-24
Author(s):  
Dequan Jin ◽  
Ziyan Qin ◽  
Murong Yang ◽  
Penghe Chen
Keyword(s):  
Numerical Experiments ◽  
State Of The Art ◽  
Neural Model ◽  
Parameter Tuning ◽  
Small Sample ◽  
Lateral Interaction ◽  
Learning Tasks ◽  
Proposed Model ◽  
High Level ◽  
Elementary Field

We propose a novel neural model with lateral interaction for learning tasks. The model consists of two functional fields: an elementary field to extract features and a high-level field to store and recognize patterns. Each field is composed of some neurons with lateral interaction, and the neurons in different fields are connected by the rules of synaptic plasticity. The model is established on the current research of cognition and neuroscience, making it more transparent and biologically explainable. Our proposed model is applied to data classification and clustering. The corresponding algorithms share similar processes without requiring any parameter tuning and optimization processes. Numerical experiments validate that the proposed model is feasible in different learning tasks and superior to some state-of-the-art methods, especially in small sample learning, one-shot learning, and clustering.

Drop-Out Risk Measurement of E-Banking Customers

2014 ◽  
pp. 330-349
Author(s):  
Juan Lara-Rubio ◽  
Myriam Martínez-Fiestas ◽  
Antonio M. Cortés-Romero
Keyword(s):  
Statistical Model ◽  
Financial Institutions ◽  
Financial Institution ◽  
Risk Measurement ◽  
Drop Out ◽  
High Rate ◽  
Dropping Out ◽  
Proposed Model ◽  
Current Financial Crisis ◽  
High Level

During the last decade, the national financial markets have shown a great transformation that has failed to reduce the high rate of existing banking in spite of the current financial crisis. This high level of competition makes financial institutions concerned about the loyalty of their customers to maintain or increase their market share and profitability. In this chapter, the authors propose a statistical model that measures the risk of customers dropping out of a Spanish financial institution, and this is a widespread method for the financial sector in general. The risk depends on socio-demographic and economic factors, as well as—most importantly—on the levels of satisfaction and trust that the bank produces in customers. Research shows that the proposed model can help institutions to know which customers have a greater risk of dropping out and, therefore, establish some recommendations for their loyalty.

scholarly journals Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 988
Author(s):  
Ho-Seung Cha ◽  
Chang-Hee Han ◽  
Chang-Hwan Im
Keyword(s):  
Resting State ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Mental States ◽  
Computer Interface ◽  
Practical Applications ◽  
Eeg Data ◽  
Brain States ◽  
Electroencephalogram Eeg ◽  
Eeg Features

With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.

scholarly journals Graph Dilated Network with Rejection Mechanism

2020 ◽  
Vol 10 (7) ◽  
pp. 2421
Author(s):  
Bencheng Yan ◽  
Chaokun Wang ◽  
Gaoyang Guo
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Great Success ◽  
Convolution Kernel ◽  
Graph Structure ◽  
Proposed Model ◽  
Rejection Mechanism ◽  
Laplacian Smoothing ◽  
Graph Neural Networks ◽  
High Level

Recently, graph neural networks (GNNs) have achieved great success in dealing with graph-based data. The basic idea of GNNs is iteratively aggregating the information from neighbors, which is a special form of Laplacian smoothing. However, most of GNNs fall into the over-smoothing problem, i.e., when the model goes deeper, the learned representations become indistinguishable. This reflects the inability of the current GNNs to explore the global graph structure. In this paper, we propose a novel graph neural network to address this problem. A rejection mechanism is designed to address the over-smoothing problem, and a dilated graph convolution kernel is presented to capture the high-level graph structure. A number of experimental results demonstrate that the proposed model outperforms the state-of-the-art GNNs, and can effectively overcome the over-smoothing problem.

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