Abstract-In this paper we present a system that uses the human ability to control a video game on a mobile device using electroencephalographic (EEG) Mu rhythms. The signals were obtained using a specially designed electrode cap and equipment, and sent through a Bluetooth connection to a PC that processes it in real time. The signal was then mapped onto two control signals and sent through wireless connection to a mobile gaming device BreakOut − . We have also investigated the human's ability to play the video game by manipulating neuronal motor cortex activity in the presence of a visual feedback environment. The participants played the video game by using their thoughts only with up to 80% accuracy over controlling the target.

Recommendations to connect like-minded people can result in increased engagement amongst members of online communities, thus playing an important role in their sustainability. We have developed a suite of algorithms for friend recommendations using a social trust model called STrust. In STrust, the social trust of individual members is derived from their behaviours in the community. The unique features of our friend recommendation algorithms are that they capture different behaviours by (a) distinguishing between passive and active behaviours, (b) classifying behaviours as contributing to users' popularity or engagement and (c) considering different member activities in a variety of contexts. In this paper, we present our social trust based recommendation algorithms and evaluate them against algorithms based on the social graph (such as Friends-Of-A-Friend). We use data collected from the online CSIRO Total Wellbeing Diet portal which has been trialled by over 5,000 Australians over a 12 week period. Our results show that social trust based recommendation algorithms outperform social graph based algorithms.

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