Classification of EEG signals of user states in gaming using machine learning

In this research, brain activity of user states was analyzed using machine learning algorithms. When a user interacts with a computer-based system including playing computer games like Tetris, he or she may experience user states such as boredom, flow, and anxiety. The purpose of this research is to apply machine learning models to Electroencephalogram (EEG) signals of three user states -- boredom, flow and anxiety -- to identify and classify the EEG correlates for these user states. We focus on three research questions: (i) How well do machine learning models like support vector machine, random forests, multinomial logistic regression, and k-nearest neighbor classify the three user states -- Boredom, Flow, and Anxiety? (ii) Can we distinguish the flow state from other user states using machine learning models? (iii) What are the essential components of EEG signals for classifying the three user states? To extract the critical components of EEG signals, a feature selection method known as minimum redundancy and maximum relevance method was implemented. An average accuracy of 85 % is achieved for classifying the three user states by using the support vector machine classifier --Abstract, page iii

Neural correlates of flow, boredom, and anxiety in gaming: An electroencephalogram study

Games are engaging and captivating from a human-computer interaction (HCI) perspective as they can facilitate a highly immersive experience. This research examines the neural correlates of flow, boredom, and anxiety during video gaming. A within-subject experimental study (N = 44) was carried out with the use of electroencephalogram (EEG) to assess the brain activity associated with three states of user experience - flow, boredom, and anxiety - in a controlled gaming environment. A video game, Tetris, was used to induce flow, boredom, and anxiety. A 64 channel EEG headset was used to track changes in activation patterns in the frontal, temporal, parietal, and occipital lobes of the players\u27 brains during the experiment. EEG signals were pre-processed and Fast Fourier Transformation values were extracted and analyzed. The results suggest that the EEG potential in the left frontal lobe is lower in the flow state than in the resting and boredom states. The occipital alpha is lower in the flow state than in the resting state. Similarly, the EEG theta in the left parietal lobe is lower during the flow state than the resting state. However, the EEG theta in the frontal-temporal region of the brain is higher in the flow state than in the anxiety state. The flow state is associated with low cognitive load, presence of attention levels, and loss of self-consciousness when compared to resting and boredom states --Abstract, page iii

Upsie: a mobile app on financial education for teenagers

Teenagers live in a world surrounded by technological devices that are constantly connected to the Internet. They are born instinctively knowing how to use these devices and quickly cannot live without them. This constant online presence and open access to social media full of advertisement campaigns, encourage consumerist behaviors at an increasingly early age. However, teenagers often do not know how to manage money or have a critical opinion on their consuming habits. This problem motivated a research project with the goal of creating an educational and serious mobile app, using gamification elements, to help teenagers manage their financial expenses, as well as educating their consuming habits towards more conscious and informed decisions. Upsie is a mobile app that can be used on a daily basis, and where gamification elements are used to generate more engagement in users. From user interviews to interface design and usability testing, the entire process of creating the app is covered, until reaching a final interactive prototype.Os adolescentes vivem rodeados de dispositivos tecnológicos que estão constantemente ligados à Internet. Os jovens nascem a saber como usar estes dispositivos quase instintivamente e rapidamente não conseguem viver sem eles. A constante presença online e o acesso a redes sociais repletas de campanhas publicitárias, estimulam comportamentos consumistas numa idade cada vez mais precoce. No entanto, os adolescentes muitas vezes não sabem como gerir o seu dinheiro ou não têm uma opinião crítica sobre os seus hábitos de consumo. Esta questão motivou um projeto de investigação com o objetivo de criar uma aplicação de telemóvel séria e educativa que ajuda os adolescentes a gerir as suas despesas financeiras, para além de educar os seus hábitos de consumo, promovendo decisões mais conscientes e informadas. Upsie é uma aplicação que pode ser usada no dia-a-dia, e onde os elementos de gamificação vêm gerar mais motivação nos utilizadores. Desde entrevistas a utilizadores, até ao desenho da interface, passando pela realização de testes de usabilidade, todo o processo de desenvolvimento desta app é apresentado, até chegar ao protótipo interativo final