Interfaces Cérebro-Computador Utilização do Emotiv EPOC para controlar software lúdico

Uma interface cérebro-computador (BCI) não é mais do que um dispositivo que lê e analisa ondas cerebrais e as converte em ações sobre um computador. Com a evolução das BCI e a possibilidade de acesso às mesmas por parte do público começou a ser possível o uso de BCIs para fins lúdicos. Nesse sentido nesta tese foi feito um estudo sobre interfaces cérebro-computador, o que são, que tipos de BCI existem, o seu uso para entretenimento, as suas limitações e o futuro deste tipo de interfaces. Foi ainda criado um software lúdico controlado por BCI (Emotiv EPOC) que é composto por um jogo tipo Pong e um reprodutor de música. O reprodutor de música através de BCI classifica e recomenda músicas ao utilizador. Com esta tese foi possível chegar à conclusão que é possível utilizar BCI para entretenimento (jogos e recomendação de conteúdos) apesar de se ter verificado que para jogos os dispositivos tradicionais de controlo (rato e teclado) ainda têm uma precisão muito superior.A brain-computer interface (BCI) is no more than a device that reads and analyzes brain waves and converts them into actions on a computer. With the evolution of the BCI and the possibility of access to them by the public began to allow the use of BCIs for entertainment purposes. Therefore, this thesis presents a study about brain-computer interfaces, what they are, types of BCI that exist, its use for entertainment, their limitations and the future of this type of interface. It was also created an entertainment software controlled by BCI (Emotiv EPOC) that includes a game like Pong and a music player. The music player through BCI ranks and recommends songs to the user. With this thesis was concluded that it is possible to use BCI for entertainment (games and content recommendation) although it was verified that the traditional gaming control devices (mouse and keyboard) still have a much higher accuracy

Designing recommendation agent

Ph.DDOCTOR OF PHILOSOPH

Modeling User Preferences and Mediating Agents in Electronic Commerce

An important ingredient in agent-mediated Electronic Commerce is the presence of intelligent mediating agents that assist Electronic Commerce participants (e.g., individual users, other agents, organisations). These mediating agents are in principle autonomous agents that will interact with their environments (e.g. other agents and web-servers) on behalf of participants who have delegated tasks to them. For mediating agents a (preference) model of participants is indispensable. In this paper, a generic mediating agent architecture is introduced. Furthermore, we discuss our view of user preference modeling and its need in agent-mediated electronic commerce. We survey the state of the art in the field of preference modeling and suggest that the preferences of electronic commerce participants can be modelled by learning from their behaviour. In particular, we employ an existing machine learning method called inductive logic programming (ILP). We argue that this method can be used by mediating agents to detect regularities in the behaviour of the involved participants and induce hypotheses about their preferences automatically. Finally, we discuss some advantages and disadvantages of using inductive logic programming as a method for learning user preferences and compare this method with other approaches