scholarly journals Correlation of learning disabilities to porn addiction based on EEG

2021 ◽  
Vol 10 (1) ◽  
pp. 148-155
Author(s):  
Norhaslinda Kamaruddin ◽  
Nurul Izzati Mat Razi ◽  
Abdul Wahab
Keyword(s):  
Direct Analysis ◽  
Learning Disorders ◽  
Intellectual Ability ◽  
Learning Disorder ◽  
Negative Effects ◽  
Brain Wave ◽  
Hyperactivity Disorder ◽  
Psychological Instruments ◽  
Electroencephalogram Eeg ◽  
The Brain

Researchers were able to correlate porn addiction based on electroencephalogram (EEG) signal analysis to the psychological instruments’ findings. In this paper we attempt to correlate the porn addiction to various cases of learning disorders through analyzing EEG signals. Since porn addiction involved the brainwave power at the frontal of the brain, which reflects the executive functions, this may have correlation to learning disorder. Only three types of learning disorder will be of interest in our study involving dyslexic, attention deficit and hyperactivity disorder (ADHD) and autistic children because they involved reduced intellectual ability observed from the lack of listening, speaking, reading, writing, reasoning, or mathematical proficiencies. Children with such disorder when expose to the internet unfiltered porn contents may have minimal understanding of the negative effects of the contents. Such unmonitored exposure to pornographic contents may result to porn addiction because it may trigger excitement and induced pleasure. Experimental results show strong correlation of learning disorders to porn addiction, which can be worthwhile for further analysis. In addition, this paper also indicates that analyzing brainwave patterns could provide a better insight into predicting and detecting children with learning disorders and addiction with direct analysis of the brain wave patterns.

scholarly journals Deep Learning on Game Addiction Detection Based on Electroencephalogram

2021 ◽  
Vol 5 (3) ◽  
pp. 963
Author(s):  
Lalu Arfi Maulana Pangistu ◽  
Ahmad Azhari
Keyword(s):  
Brain Activity ◽  
Late Adolescence ◽  
Wave Activity ◽  
Test Results ◽  
Brain Wave ◽  
Brain Waves ◽  
Game Addiction ◽  
Brain Wave Activity ◽  
Electroencephalogram Eeg ◽  
The Brain

Playing games for too long can be addictive. Based on a recent study by Brand et al, adolescents are considered more vulnerable than adults to game addiction. The activity of playing games produces a wave in the brain, namely beta waves where the person is in a focused state. Brain wave activity can be measured and captured using an Electroencephalogram (EEG). Recording brain wave activity naturally requires a prominent and constant brain activity such as when concentrating while playing a game. This study aims to detect game addiction in late adolescence by applying Convolutional Neural Network (CNN). Recording of brain waves was carried out three times for each respondent with a stimulus to play three different games, namely games included in the easy, medium, and hard categories with a consecutive taking time of 10 minutes, 15 minutes, and 30 minutes. Data acquisition results are feature extraction using Fast Fourier Transform to get the average signal for each respondent. Based on the research conducted, obtained an accuracy of 86% with a loss of 0.2771 where the smaller the loss value, the better the CNN model built. The test results on the model produce an overall accuracy of 88% with misclassification in 1 data. The CNN model built is good enough for the detection of game addiction in late adolescence. 

scholarly journals Introduction to the identification of brain waves based on their frequency

2018 ◽  
Vol 210 ◽  
pp. 05012 ◽  
Author(s):  
Zuzana Koudelková ◽  
Martin Strmiska
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Brain Wave ◽  
Non Invasive ◽  
Brain Waves ◽  
Analytical Reasoning ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Research Type

A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.

scholarly journals Intelligent System of Somatosensory Music Therapy Information Feedback in Deep Learning Environment

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Nan Zhao
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Music Perception ◽  
Intelligent System ◽  
Recognition Performance ◽  
Brain Wave ◽  
Traditional Classification ◽  
Extraction Model ◽  
Electroencephalogram Eeg ◽  
The Brain

In the treatment of children with autistic spectrum disorder (ASD) through music perception, the perception effect and the development of the disease are mainly reflected in the fluctuations of the electroencephalogram (EEG), which is clinically effective on the brain. There is an inaccuracy problem in electrogram judgment, and deep learning has great advantages in signal feature extraction and classification. Based on the theoretical basis of Deep Belief Network (DBN) in deep learning, this paper proposes a method that combines the optimized Restricted Boltzmann machine (RBM) feature extraction model with the softmax classification algorithm. Brain wave tracking analysis is performed on children with autism who have received different music perception treatments to improve classification accuracy and achieve the purpose of accurately judging the condition. Through continuous adjustment and optimization of the weight matrix in the model, a stable recognition model is obtained. The simulation results show that this optimization algorithm can effectively improve the recognition performance of DBN, with an accuracy of 94% in a certain environment, and has a better classification effect than other traditional classification methods.

scholarly journals Role of Gut Microbiota in the Pathophysiology of Stress-Related Disorders: Evidence from Neuroimaging Studies

2021 ◽  
pp. 1-7
Author(s):  
Satoshi Izuno ◽  
Kazufumi Yoshihara ◽  
Nobuyuki Sudo
Keyword(s):  
Gut Microbiota ◽  
Emotional Response ◽  
Scientific Evidence ◽  
Negative Effects ◽  
Psychosomatic Disorder ◽  
Hyperactivity Disorder ◽  
Probiotic Intervention ◽  
Subcortical Regions ◽  
Irritable Bowel ◽  
The Brain

<b><i>Background:</i></b> The brain and gut communicate bidirectionally via immune, neurological, and endocrine pathways, which is termed the “brain-gut interaction.” Recent studies of gut microbiota as a mediator of this interaction have provided a growing body of scientific evidence that suggests that the gut microbiota influences stress and emotional responses and stress-related disorders. <b><i>Summary:</i></b> Major advances in analytical methods have led to an increased number of studies that combine gut microbiota and neuroimaging, mainly magnetic resonance imaging, to elucidate the mechanisms. Observational studies have been done to examine brain characteristics related to gut microbiota profiles, and intervention studies have examined brain changes related to probiotic intake. Studies of healthy subjects using negative emotional stimuli have shown that the pattern of emotional response differs depending on the gut microbiota profile and that probiotic intervention can modulate emotional response and be a buffer against the negative effects of stress. In studies on irritable bowel syndrome (IBS), a typical psychosomatic disorder, IBS-specific gut microbiota were reported to contribute to visceral irritability and pain by affecting the subcortical regions. Studies on psychiatric disorders revealed that a relative abundance of <i>Bacteroides</i> that produce γ-aminobutyric acid in feces was associated with a change in brain function specific to depression and that gut microbiota have an influence on abnormalities in the reward system of attention-deficit/hyperactivity disorder.

scholarly journals Visual perception skill profile pattern in children with learning disorder

2020 ◽  
Vol 7 (9) ◽  
pp. 1860
Author(s):  
Ankita Patel ◽  
Mona Gajre ◽  
Prashant Bhandarkar ◽  
Vyankatesh Parlikar
Keyword(s):  
Visual Perception ◽  
Visual Discrimination ◽  
Visual Memory ◽  
Tertiary Hospital ◽  
Medical Professional ◽  
Learning Disorders ◽  
Learning Disorder ◽  
Hyperactivity Disorder ◽  
Poor Academic Performance ◽  
Perception Score

Background: Visual perception skill related problems are important in poor academic performance in learning disability (LD) children. Visual perception skill often not tested in LD children. The objective of the study is to explore visual perception skill pattern among children with learning disorder.Methods: Retrospective observational study was conducted at LD clinic of tertiary hospital. Children diagnosed with learning disorder were includes. Visual perception data were collected using predefined standard questionnaire of third edition total visual perception score (TVPS-3). Trained medical professional collected the details.Results: Total 103 children diagnosed with LD were evaluated for TVPS-3. Majority of the children had all three learning disorders-dyslexia, dysgraphia and dyscalculia. 58.42% children had co-morbid attention deficit hyperactivity disorder. From the 7 subtests of the TVPS visual discrimination, visual memory, form constancy and visual figure - ground affected more in boys and also in lower age children.Conclusions: Assessment of visual perception skill in children with learning disorder is crucial. Visual perception rehabilitation with other management of LD can benefit the overall functionality of these children.

TIMELESS BUILDINGS AND THE HUMAN BRAIN: The Effect of Spiritual Spaces on Human Brain Waves

2014 ◽  
Vol 8 (1) ◽  
pp. 133
Author(s):  
Sally M. Essawy ◽  
Basil Kamel ◽  
Mohamed S. Elsawy
Keyword(s):  
Human Brain ◽  
Case Studies ◽  
Spiritual Experience ◽  
Brain Wave ◽  
Human Comfort ◽  
Beliefs And Practices ◽  
Brain Waves ◽  
Space Design ◽  
State Of Mind ◽  
The Brain

Some buildings hold certain qualities of space design similar to those originated from nature in harmony with its surroundings. These buildings, mostly associated with religious beliefs and practices, allow for human comfort and a unique state of mind. This paper aims to verify such effect on the human brain. It concentrates on measuring brain waves when the user is located in several spots (coordinates) in some of these buildings. Several experiments are conducted on selected case studies to identify whether certain buildings affect the brain wave frequencies of their users or not. These are measured in terms of Brain Wave Frequency Charts through EEG Device. The changes identified on the brain were then translated into a brain diagram that reflects the spiritual experience all through the trip inside the selected buildings. This could then be used in architecture to enhance such unique quality.

Disorders of Development

2010 ◽  
pp. 551-574
Author(s):  
V. Mark Durand
Keyword(s):  
Pervasive Developmental Disorders ◽  
Developmental Disorders ◽  
Early Adulthood ◽  
Learning Disorders ◽  
Future Research ◽  
Hyperactivity Disorder ◽  
Asperger Disorder ◽  
Oppositional Defiant ◽  
Assessment And Treatment ◽  
Clinically Significant

Disorders of development include a range of problems first evidenced in childhood. Although most disorders have their origins in childhood, a few fully express themselves before early adulthood. This chapter describes the nature, assessment, and treatment of the more common disorders that are revealed in a clinically significant way during a child’s developing years. The disorders of development affect a range of functioning, from single skills deficits to more pervasive problems that negatively impact a child’s ability to function. Included is coverage of several disorders usually diagnosed first in infancy, childhood, or adolescence, including attention-deficit hyperactivity disorder, oppositional defiant disorder, conduct disorder, learning disorders, communication and related disorders, pervasive developmental disorders (including autistic disorder and Asperger disorder), and intellectual disabilities. Recommendations for future research on the potential for advancing knowledge regarding spectrums within some of these disorders, as well as recommendations for treatment, are outlined.

scholarly journals SHR/NCrl rats as a model of ADHD can be discriminated from controls based on their brain, blood, or urine metabolomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Camille Dupuy ◽  
Pierre Castelnau ◽  
Sylvie Mavel ◽  
Antoine Lefevre ◽  
Lydie Nadal-Desbarats ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Attention Deficit Hyperactivity Disorder ◽  
Animal Model ◽  
Metabolic Pathways ◽  
Neurodevelopmental Disorder ◽  
Hyperactivity Disorder ◽  
Pathophysiological Condition ◽  
The Brain ◽  
And Oxidative Stress ◽  
Peripheral Samples

AbstractAttention-Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorder characterized by inattention, impulsivity, and hyperactivity. The neurobiological mechanisms underlying ADHD are still poorly understood, and its diagnosis remains difficult due to its heterogeneity. Metabolomics is a recent strategy for the holistic exploration of metabolism and is well suited for investigating the pathophysiology of diseases and finding molecular biomarkers. A few clinical metabolomic studies have been performed on peripheral samples from ADHD patients but are limited by their access to the brain. Here, we investigated the brain, blood, and urine metabolomes of SHR/NCrl vs WKY/NHsd rats to better understand the neurobiology and to find potential peripheral biomarkers underlying the ADHD-like phenotype of this animal model. We showed that SHR/NCrl rats can be differentiated from controls based on their brain, blood, and urine metabolomes. In the brain, SHR/NCrl rats displayed modifications in metabolic pathways related to energy metabolism and oxidative stress further supporting their importance in the pathophysiology of ADHD bringing news arguments in favor of the Neuroenergetic theory of ADHD. Besides, the peripheral metabolome of SHR/NCrl rats also shared more than half of these differences further supporting the importance of looking at multiple matrices to characterize a pathophysiological condition of an individual. This also stresses out the importance of investigating the peripheral energy and oxidative stress metabolic pathways in the search of biomarkers of ADHD.

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