Brain developmental disorders’ modelling based on preschoolers neuro-physiological profiling

Frequently misunderstood by their teachers as being low performers, children with learning disabilities (LDs) such as dyslexia, ADHD, and Asperger’s Syndrome develop low self-confidence and poor self-esteem that may lead to the risk of developing psychological and emotional problems. On contrary, research has shown that a substantial number of these children are capable of learning, and hence, are high-functioning. Therefore, there is a need to provide for the early detection of LDs and instruction that focuses on their needs based on their profiles. Profiling is normally done through observations on the psychological manifestations of LDs by parents and teachers as third-party observers. The first party experience, which is reflected through brain manifestations, is often overlooked. Hence the aim of this paper is to present an alternative solution to profile young children with LDs using electroencephalogram (EEG) that capture brain signals to measure brain functionalities and correlate them with the different LDs. Studies on neurophysiological signals and their relationship to LDs are used to develop Computational Neuro-Physiological (CN-P) model to be an alternative in quantifying the children brain activation function related to learning experience. It is envisaged that such model can profile children with learning disabilities to provide effective intervention in timely manner which can help teachers to provide differentiated instruction for children with LDs. This is in line with the thrust of the Education National Key Result Area (NKRA), the Malaysia Education Blueprint 2013-2025, and the Special Education Regulations 2013

Investigation on Dynamic Speech Emotion from the Perspective of Brain Associative Memory

AbstractMany researchers have studied speech emotion for years from the perspective of psychology to engineering. To date, none has made the speech emotion recognition system intuitive enough in such a way that it can be embedded in automatic answering machines that can effectively detect the various affective states of human verbal communication. In most cases the underlying emotional information was misinterpreted thus resulting in wrong feedbacks and responses. The complexity of understanding and analyzing speech emotion is presented in the dynamics of the emotion itself. Emotion is dynamic and changeable over time. Hence, it is imperative to cater for this parameter to boost the performance of the speech emotion recognition system. In this paper, values of Valence (V) and Arousal(A) are used to generate a recalibrated affective space model. Such approach is adopted from psychologists’ understanding that emotion can be represented using emotion primitives’ values. The VA approach is then coupled with the brain associative memory concept that can provides a better means in understanding the dynamics of speech emotion. Results of such analysis tallies with the psychological findings and has its practical implementation

Classification of dyslexic and normal children during resting condition using KDE and MLP

Dyslexia is a specific reading disability. It can be characterized by a severe difficulty in reading, learning, spelling, memorizing as well as sequencing activities. In this work, the participants' electroencephalogram (EEG) signals were monitored during resting situation. These signals are captured from the scalp of each subject to measure the brain activities during both eyes opened and eye closed scenarios. Features from the EEG signals were extracted using the Kernel Density Estimation (KDE) and classified using the Multilayer Perceptron (MLP). Due to the large number of features extracted, relevant features are then selected by grouping various spectral components and eliminating irrelevant features. For a comparison purpose, brain signals of three children who are diagnosed of having dyslexia by medical practitioners (denoted as dyslexic) and the other three children diagnosed otherwise (denoted as normal) are used. Experimental results shown that there is a clear distinction between dyslexic and normal children during both eyes closed and eyes opened scenario. Hence, further works can be extended for early intervention in such a way that these children can be further assisted to cope with their learning experience

Neuro-physiological porn addiction detection using machine learning approach

Pornography is a portrayal of sexual subject contents for the exclusive purpose of sexual arousal that can lead to addiction. The Internet accessibility has created unprecedented opportunities for sexual education, learning, and growth. Hence, the risk of porn addiction developed by teenagers has also increased due to highly prevalent porn consumption. To date, the only available means of detecting porn addiction is through questionnaire. However, while answering the questions, participants may suppress or exaggerate their answers because porn addiction is considered taboo in the community. Hence, the purpose of this project is to develop an engine with multiple classifiers to recognize porn addiction using electroencephalography signals and to compare classifiers performance. In this work, three different classifiers of Multilayer Perceptron, Naive Bayesian, and Random Forest are employed. The experimental results show that the MLP classifier yielded slightly better accuracy compared to Naïve Bayes and Random Forest classifiers making the MLP classifier preferable for porn addiction recognition. Although this work is still at infancy stage, it is envisaged for the work to be expanded for comprehensive porn addiction recognition system so that early intervention and appropriate support can be given for the teenagers with pornography addiction problem. Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved

Pre-trial process in criminal proceedings

Pre-trial process in criminal proceedings is a specially designed edition for students who are pursuing law in various universities and private colleges. The study of criminal Proceedings under the Criminal Procedure Code, has been made easy by this book. It will be equally useful to legal practitioners and academicians

Extracting features using computational cerebellar model for emotion classification

Several feature extraction techniques have been employed to extract features from EEG signals for classifying emotions. Such techniques are not constructed based on the understanding of EEG and brain functions, neither inspired by the understanding of emotional dynamics. Hence, the features are difficult to be interpreted and yield low classification performance. In this study, a new feature extraction technique using Cerebellar Model Articulation Controller (CMAC) is proposed. The features are extracted from the weights of datadriven self-organizing feature map that are adjusted during training to optimize the error obtained from the desired output and the calculated output. Multi-Layer Perceptron (MLP) classifier is then employed to perform classification on fear, happiness, sadness and calm emotions. Experimental results show that the average accuracy of classifying emotions from EEG signals captured on 12 children aged between 4 to 6 years old ranging from 84.18% to 89.29%. In addition, classification performance for features derived from other techniques such as Power Spectrum Density (PSD), Kernel Density Estimation (KDE) and Mel-Frequency Cepstral Coefficients (MFCC) are also presented as a standard benchmark for comparison purpose. It is observed that the proposed approach is able to yield accuracy of 33.77% to 55% as compared to the respective comparison features. The experimental results indicated that the proposed approach has potential for comparative emotion recognition accuracy when coupled with MLP