Perceived Stress and Cfognition Function Quantification in a Scary Video Game: An Electroencephalogram Features and Biochemical Measures.

Introduction: Video games affect the stress system and cognitive abilities in different ways. Here, we evaluated electrophysiological and biochemical indicators of stress and assessed their effects on cognition and behavioral indexes after playing a scary video game. Methods: Thirty volunteers were recruited into two groups as control and experimental. The saliva and blood samples were collected before and after intervention (watching/playing the scary game for control and experimental groups respectively). To measure cortisol and salivary alpha-amylase (sAA) levels, oxytocin (OT), and brain-derived neurotrophic factor (BDNF) plasma levels, dedicated ELISA kits were used. Electroencephalography recording was done before and after interventions for electroencephalogram (EEG)-based emotion and stress recognition. Then, the feature extraction (for mental stress, arousal, and valence) was done. Matrix laboratory (MATLAB) software, version 7.0.1 was used for processing EEG-acquired data. The repeated measures were applied to determine the intragroup significance level of difference. Results: Scary gameplay increases mental stress (P<0.001) and arousal (P<0.001) features and decreases the valence (P<0.001) one. The salivary cortisol and alpha-amylase levels were significantly higher after the gameplay (P<0.001 for both). OT and BDNF plasma levels decreased after playing the scary game (P<0.05 for both). Conclusion: We conclude that perceived stress considerably elevates among players of scary video games, which adversely affects the emotional and cognitive capabilities, possibly via the strength of synaptic connections, and dendritic thorn construction of the brain neurons among players. Highlights: The mental stress level increases in players of scary video games.The salivary cortisol and alpha-amylase levels are significantly higher after the scary gameplay.Plasma levels of oxytocin and brain-derived neurotrophic factor decrease after the scary gameplay.The arousal and valence features increase in players of scary video game.Cognitive capabilities are adversely affected by the scary gameplay. Plain Language Summary: Nowadays, video games have become an important part of human life at different ages. Therefore, assessing their effects (improving and/or damaging) on cognition and behavior is important for understanding how they affect the nervous system. The results of such studies can be used to design a variety of games in the future in a way that minimizes the harmful side effects of video games on human cognitive functions and maximizes their beneficial effects.

Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals.

Achieving an efficient and reliable method is essential to interpret a user's brain wave and deliver an accurate response in biomedical signal processing. However, EEG patterns exhibit high variability across time and uncertainty due to noise and it is a significant problem to be addressed in mental task as motor imagery. Therefore, fuzzy components may help to enable a higher tolerance to noisy conditions. With the advent of Deep Learning and its considerable contributions to Artificial intelligence and data analysis, numerous efforts have been made to evaluate and analyze brain signals. In this study, to make use of neural activity phenomena, the feature extraction preprocessing is applied based on Multi-scale filter bank CSP. In the following, the hybrid series architecture named EEG-CLFCNet is proposed which extract the frequency and spatial features by Compact-CNN and the temporal features by the LSTM network. However, the classification results are evaluated by merging the fully connected network and fuzzy neural block. Here, the proposed method is further validated by the BCI competition IV-2a dataset and compare with two hyperparameter tuning methods, Coordinate-descent and Bayesian optimization algorithm. The proposed architecture that used fuzzy neural block and Bayesian optimization as tuning approach, results in better classification accuracy compared with the state-of-the-art literatures. As results shown, the remarkable performance of the proposed model, EEG-CLFCNet, and the general integration of fuzzy units to other classifiers would pave the way for enhanced MI-based BCI systems.

Causal associations of genetic factors with clinical progression in amyotrophic lateral sclerosis.

BACKGROUND AND OBJECTIVE: Recent advances in the genetic causes of ALS reveals that about 10% of ALS patients have a genetic origin and that more than 30 genes are likely to contribute to this disease. However, four genes are more frequently associated with ALS: C9ORF72, TARDBP, SOD1, and FUS. The relationship between genetic factors and ALS progression rate is not clear. In this study, we carried out a causal analysis of ALS disease with a genetics perspective in order to assess the contribution of the four mentioned genes to the progression rate of ALS. METHODS: In this work, we applied a novel causal learning model to the CRESLA dataset which is a longitudinal clinical dataset of ALS patients including genetic information of such patients. This study aims to discover the relationship between four mentioned genes and ALS progression rate from a causation perspective using machine learning and probabilistic methods. RESULTS: The results indicate a meaningful association between genetic factors and ALS progression rate with causality viewpoint. Our findings revealed that causal relationships between ALSFRS-R items associated with bulbar regions have the strongest association with genetic factors, especially C9ORF72; and other three genes have the greatest contribution to the respiratory ALSFRS-R items with a causation point of view. CONCLUSIONS: The findings revealed that genetic factors have a significant causal effect on the rate of ALS progression. Since C9ORF72 patients have higher proportion compared to those carrying other three gene mutations in the CRESLA cohort, we need a large multi-centric study to better analyze SOD1, TARDBP and FUS contribution to the ALS clinical progression. We conclude that causal associations between ALSFRS-R clinical factors is a suitable predictor for designing a prognostic model of ALS.

Predicting Parkinson's Disease Progression: Evaluation of Ensemble Methods in Machine Learning.

Parkinson's disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.

Recommendation agents and information sharing through social media for coronavirus outbreak.

The novel outbreak of coronavirus disease (COVID-19) was an unexpected event for tourism in the world as well as tourism in the Netherlands. In this situation, the travelers' decision-making for tourism destinations was heavily affected by this global event. Social media usage has played an essential role in travelers' decision-making and increased the awareness of travel-related risks from the COVID-19 outbreak. Online consumer media for the outbreak of COVID-19 has been a crucial source of information for travelers. In the current situation, tourists are using electronic word of mouth (eWOM) more and more for travel planning. Opinions provided by peer travelers for the outbreak of COVID-19 tend to reduce the possibility of poor decisions. Nevertheless, the increasing number of reviews per experience makes reading all feedback hard to make an informed decision. Accordingly, recommendation agents developed by machine learning techniques can be effective in the analysis of such social big data for the identification of useful patterns from the data, knowledge discovery, and real-time service recommendations. The current research aims to adopt a framework for the recommendation agents through topic modeling to uncover the most important dimensions of COVID-19 reviews in the Netherland forums in TripAdvisor. This study demonstrates how social networking websites and online reviews can be effective in unexpected events for travelers' decision making. We conclude with the implications of our study for future research and practice.

What is the impact of service quality on customers' satisfaction during COVID-19 outbreak? New findings from online reviews analysis.

The COVID-19 pandemic has caused major global changes both in the areas of healthcare and economics. This pandemic has led, mainly due to conditions related to confinement, to major changes in consumer habits and behaviors. Although there have been several studies on the analysis of customers' satisfaction through survey-based and online customers' reviews, the impact of COVID-19 on customers' satisfaction has not been investigated so far. It is important to investigate dimensions of satisfaction from the online customers' reviews to reveal their preferences on the hotels' services during the COVID-19 outbreak. This study aims to reveal the travelers' satisfaction in Malaysian hotels during the COVID-19 outbreak through online customers' reviews. In addition, this study investigates whether service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. Accordingly, we develop a new method through machine learning approaches. The method is developed using text mining, clustering, and prediction learning techniques. We use Latent Dirichlet Allocation (LDA) for big data analysis to identify the voice-of-the-customer, Expectation-Maximization (EM) for clustering, and ANFIS for satisfaction level prediction. In addition, we use Higher-Order Singular Value Decomposition (HOSVD) for missing value imputation. The data was collected from TripAdvisor regarding the travelers' concerns in the form of online reviews on the COVID-19 outbreak and numerical ratings on hotel services from different perspectives. The results from the analysis of online customers' reviews revealed that service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. In addition, the results showed that although the customers are always seeking hotels with better performance, they are also concerned with the quality of related services in the COVID-19 outbreak.

The impact of coronavirus pandemic (COVID-19) on education: The role of virtual and remote laboratories in education.

To avoid the spread of the COVID-19 crisis, many countries worldwide have temporarily shut down their academic organizations. National and international closures affect over 91% of the education community of the world. E-learning is the only effective manner for educational institutions to coordinate the learning process during the global lockdown and quarantine period. Many educational institutions have instructed their students through remote learning technologies to face the effect of local closures and promote the continuity of the education process. This study examines the expected benefits of e-learning during the COVID-19 pandemic by providing a new model to investigate this issue using a survey collected from the students at Imam Abdulrahman Bin Faisal University. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed on 179 useable responses. This study applied Push-Pull-Mooring theory and examined how push, pull, and mooring variables impact learners to switch to virtual and remote educational laboratories. The Protection Motivation theory was employed to explain how the potential health risk and environmental threat can influence the expected benefits from e-learning services. The findings revealed that the push factor (environmental threat) is significantly related to perceived benefits. The pull factors (e-learning motivation, perceived information sharing, and social distancing) significantly impact learners' benefits. The mooring factor, namely perceived security, significantly impacts learners' benefits.

The Effect of Brain Teaser Games on the Attention of Players Based on Hormonal and Brain Signals Changes.

INTRODUCTION: Computer games as an interactive media play a significant role in the cognitive and behavioral health of the players. Computer games have either positive or negative effects on cognitive indices among players. They also directly influence the lifestyle and quality of life of children, adolescents, and young adults. The present study aimed to evaluate the short-term effects of the brain teaser game on players. METHODS: Among 45 male volunteers, 40 subjects with an average age of 20 years were recruited and divided into two groups: the experimental group and the control group. All required tests were conducted before and after the intervention (playing the game) on the experimental group. Also, the same tests were performed on the control group, in which the participants were not allowed to play the game. All participants completed a questionnaire comprised demographic characteristics and specific information regarding the game (e.g., game style and hours spent on playing the game). The saliva samples were collected to measure levels of cortisol and α-amylase. The salivary α-amylase (sAA) and cortisol levels were analyzed using the relevant ELISA kits. The cognitive tests were performed using PASAT software before and after the game to assess the perceptual-cognitive abilities of the players. The brain waveforms were acquired by a 14-channel Emotiv brain signal recording device before and after the game. Data analysis was conducted in R and MATLAB software. RESULTS: PASAT test suggested that mental health and sustained attention were significantly improved after the intervention. In addition, the sAA and salivary cortisol levels were significantly higher before the intervention. The results of the brainwave analysis revealed that stress index and attention were significantly higher before the intervention. CONCLUSION: Findings of the present study suggest that brain teaser games positively influence the central nervous system and activate stress path, leading to changes in brain signals and subsequently improved cognitive elements, such as attention among players.

Changes in Cognitive Functions Following Violent and Football Video Games in Young Male Volunteers by Studying Brain Waves.

INTRODUCTION: This research investigated the effects of violent and football video games on cognitive functions, cortisol levels, and brain waves. METHODS: A total of 64 participants competed in a single-elimination tournament. Saliva samples of all players were obtained before and after the games for the assessment of cortisol levels. The cognitive performances of the players were also assessed by paced auditory serial addition test. Moreover, the electroencephalogram recording was conducted during the games. RESULTS: The results showed that salivary cortisol levels significantly decreased after playing both games. Also, playing the football game increased reaction time, whereas decreased sustained attention and mental fatigue. CONCLUSION: Conversely, following playing a violent game, the reaction time decreased, and sustained attention and mental fatigue increased. Furthermore, the results of the EEG recording revealed that playing a violent game engaged more brain regions than the football game. In conclusion, playing violent game more effectively improved cognitive performances in the players than the football game.

The Beneficial or Harmful Effects of Computer Game Stress on Cognitive Functions of Players.

INTRODUCTION: Video games are common cultural issues with great influence in all societies. One of the important cognitive effects of video games is on creating stress on video players. The present research objective was to study different types of stress in players based on video game styles. METHODS: A total of 80 players, aged 18 to 30 years, played four types of video games; Runner game, Excitement game, Fear game, and Puzzle game. In the beginning, the players filled in the form of personal information as well as some general and specialized information on the games. Before starting each game, the saliva samples of the players were collected to measure their level of cortisol and α-amylase. At the end of each game, the same samples were collected again. The concentrations of cortisol and α-amylase were measured using a specialized kit and an ELISA device. In addition, the variations of brain waves were recorded by an Emotiv system. Finally, the data were analyzed in SPSS and Matlab system (after and before playing video game). RESULTS: The research findings revealed that the salivary α-amylase concentration increased significantly after playing the Fear game, Runner game, and Excitement game and decreased significantly after playing the Puzzle game. Moreover, the concentration of salivary cortisol increased significantly after playing the Runner game, Excitement game, and Fear game and decreased significantly after playing the Puzzle game. The brain wave analysis also revealed that the level of stress experienced by playing Fear game was higher than the Excitement game. CONCLUSION: According to the research findings, video games can affect the stress system as well as the cognitive system of humans depending on the game style. In addition, the type and level of stress triggered in the players depend on the game style.

The Effects of Fifa 2015 Computer Games on Changes in Cognitive, Hormonal and Brain Waves Functions of Young Men Volunteers.

INTRODUCTION: Computer games have attracted remarkable attentions in general publics with different cultures and their effects are subject of research by cognitive neuroscientists. In the present study, possible effects of the game Fifa 2015 on cognitive performance, hormonal levels, and electroencephalographic (EEG) signals were evaluated in young male volunteers. METHODS: Thirty two subjects aged 20 years on average participated mutually in playing computer game Fifa 2015. Identification information and general knowledge about the game were collected. Saliva samples from the contestants were obtained before and after the competition. Perceptive and cognitive performance including the general cognitive health, response delay, attention maintenance, and mental fatigue were measured using PASAT test. EEG were recorded during the play using EEG device and analyzed later using QEEG. Simultaneously, the players' behavior were recorded using a video camera. Saliva cortisol levels were assessed by ELISA kit. Data were analyzed by SPSS program. RESULTS: The impact of playing computer games on cortisol concentration of saliva before and after the game showed that the amount of saliva plasma after playing the game has dropped significantly. Also the impact of playing computer games on mental health, before and after the game indicated that the number of correct answers has not changed significantly. This indicates that sustained attention has increased in participants after the game in comparison with before that. Also it is shown that mental fatigue measured by PASAT test, did not changed significantly after the game in comparison to before that. The impact of game on changes in brain waves showed that the subjects in high activity state during playing the game had higher power of the EEG signals in most of the channels in lower frequency bands in compared to normal state. DISCUSSION: The present study showed that computer games can positively affect the stress system and the perceptual-cognitive system. Even though this impact was not significant in most cases, the changes in cognitive and hormonal test and also in brain waves were visible. Hence, due to the importance of this matter, it is necessary to create control systems in selecting the types of games for playing.

Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study.

BACKGROUND: We aimed to identify the indicators of healthcare fraud and abuse in general physicians' drug prescription claims, and to identify a subset of general physicians that were more likely to have committed fraud and abuse. METHODS: We applied data mining approach to a major health insurance organization dataset of private sector general physicians' prescription claims. It involved 5 steps: clarifying the nature of the problem and objectives, data preparation, indicator identification and selection, cluster analysis to identify suspect physicians, and discriminant analysis to assess the validity of the clustering approach. RESULTS: Thirteen indicators were developed in total. Over half of the general physicians (54%) were 'suspects' of conducting abusive behavior. The results also identified 2% of physicians as suspects of fraud. Discriminant analysis suggested that the indicators demonstrated adequate performance in the detection of physicians who were suspect of perpetrating fraud (98%) and abuse (85%) in a new sample of data. CONCLUSION: Our data mining approach will help health insurance organizations in low-and middle-income countries (LMICs) in streamlining auditing approaches towards the suspect groups rather than routine auditing of all physicians.

Using data mining to detect health care fraud and abuse: a review of literature.

Inappropriate payments by insurance organizations or third party payers occur because of errors, abuse and fraud. The scale of this problem is large enough to make it a priority issue for health systems. Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient. Combining automated methods and statistical knowledge lead to the emergence of a new interdisciplinary branch of science that is named Knowledge Discovery from Databases (KDD). Data mining is a core of the KDD process. Data mining can help third-party payers such as health insurance organizations to extract useful information from thousands of claims and identify a smaller subset of the claims or claimants for further assessment. We reviewed studies that performed data mining techniques for detecting health care fraud and abuse, using supervised and unsupervised data mining approaches. Most available studies have focused on algorithmic data mining without an emphasis on or application to fraud detection efforts in the context of health service provision or health insurance policy. More studies are needed to connect sound and evidence-based diagnosis and treatment approaches toward fraudulent or abusive behaviors. Ultimately, based on available studies, we recommend seven general steps to data mining of health care claims.