Exploring Effects of Background Music in A Serious Game on Attention by Means of EEG Signals in Children

Music and Serious Games are separately useful alternative therapy methods for helping people with a cognitive disorder, including Attention Deficit Hyperactivity Disorder (ADHD). The goal of this thesis is to explore the effect of background music on children with and without ADHD. In this study, a simple Tetris game is designed with Beethoven, Mozart music, and no-music. There are different brainwave techniques for recording; among others, the electroencephalography (EEG) allows for the most efficient use of BCI. We recorded the EEG brain signals of the regular and ADHD subjects who played the Tetris we designed according to our protocol that consists of three trials with three different background music. Attention related Alpha and Beta waves of EEG signals analyzed based on time and time-frequency domain features. The changes in the data over the 1-minute Tetris game sections are investigated with the Short-time Fourier Transform (STFT) method. The results showed that music has a considerable impact on attention of children. When it comes to music types, in general, Mozart music increases Beta waves while decreasing the Alpha band waves for subjects without ADHD. On the other hand, Beethoven music increased both Alpha and Beta band values for children with ADHD

Semi-Automated Data Labeling for Activity Recognition in Pervasive Healthcare

Activity recognition, a key component in pervasive healthcare monitoring, relies on classification algorithms that require labeled data of individuals performing the activity of interest to train accurate models. Labeling data can be performed in a lab setting where an individual enacts the activity under controlled conditions. The ubiquity of mobile and wearable sensors allows the collection of large datasets from individuals performing activities in naturalistic conditions. Gathering accurate data labels for activity recognition is typically an expensive and time-consuming process. In this paper we present two novel approaches for semi-automated online data labeling performed by the individual executing the activity of interest. The approaches have been designed to address two of the limitations of self-annotation: (i) The burden on the user performing and annotating the activity, and (ii) the lack of accuracy due to the user labeling the data minutes or hours after the completion of an activity. The first approach is based on the recognition of subtle finger gestures performed in response to a data-labeling query. The second approach focuses on labeling activities that have an auditory manifestation and uses a classifier to have an initial estimation of the activity, and a conversational agent to ask the participant for clarification or for additional data. Both approaches are described, evaluated in controlled experiments to assess their feasibility and their advantages and limitations are discussed. Results show that while both studies have limitations, they achieve 80% to 90% precision

Fuzzy-based machine learning for predicting narcissistic traits among Twitter users.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Social media has provided a platform for people to share views and opinions they identify with or which are significant to them. Similarly, social media enables individuals to express themselves authentically and divulge their personal experiences in a variety of ways. This behaviour, in turn, reflects the user’s personality. Social media has in recent times been used to perpetuate various forms of crimes, and a narcissistic personality trait has been linked to violent criminal activities. This negative side effect of social media calls for multiple ways to respond and prevent damage instigated. Eysenck's theory on personality and crime postulated that various forms of crime are caused by a mixture of environmental and neurological causes. This theory suggests certain people are more likely to commit a crime, and personality is the principal factor in criminal behaviour. Twitter is a widely used social media platform for sharing news, opinions, feelings, and emotions by users. Given that narcissists have an inflated self-view and engage in a variety of strategies aimed at bringing attention to themselves, features unique to Twitter are more appealing to narcissists than those on sites such as Facebook. This study adopted design science research methodology to develop a fuzzy-based machine learning predictive model to identify traces of narcissism from Twitter using data obtained from the activities of a user. Performance evaluation of various classifiers was conducted and an optimal classifier with 95% accuracy was obtained. The research found that the size of the dataset and input variables have an influence on classifier accuracy. In addition, the research developed an updated process model and recommended a research model for narcissism classification