Experiencing Brain-Computer Interfaces

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Brain-Computer Interfaces (BCIs) are systems that extract information from the user’s brain activity and employ it in some way in an interactive system. While historically BCIs were mainly catered towards paralyzed or otherwise physically handicapped users, the last couple of years applications with a focus on entertainment meant for healthy users gained a lot of momentum in research. While from a disabled user’s perspective functionality and accuracy have been key to get a working system, especially for healthy users the user’s experience (UX) can be considered even more important. A vast amount of effort has been put into increasing the accuracy of various types of BCIs, less research has been done on the impact this accuracy has on the UX. This thesis is structured in the following way: I Introduction, II Studies and III conclusion. Within part II, 4 systems and experiments are conducted and described. With these experiments we try to show what the influence of accuracy on the UX is. In the first chapter of Part II we outline an experiment in which a large group of users played a simulation of various levels of control (similar to the way a BCI works). This online experiment consisted of a rather simple game in which users controlled a fictituous hamster with a fixed amount of control. After every level of control the user answered a short questionnaire on UX. We found that for the lower half of the control scale, a linear relationship exists between control and the fun a user experiences. The interesting finding was that for higher levels of control, fun reaches an optimum and even tends to decrease above a certain level of control. The non perfect control could make the game more interesting to people, an indiciation that we can use a BCI to make a game more challenging and interesting. In the second chapter we describe an experiment which compares the modalities of using actual movements and imagined movements (or Motor Imagery, MI) in an Event Related (De)Synchronization (ERD/ERS) based BCI. Within the group of 20 participants the average accuracy was higher for actual movement, but most participants found imagined movement to be more challenging and fun. MI is a popular paradigm within the field of BCI, especially physically handicapped or paralyzed users can still use their brain activity to control such a system. For healthy users however, we can still use the signals from actual movements as well. Which and why signals fare better is discussed in this chapter. Building on the first and second chapters in which we simulated a BCI and in which the input was solely from a BCI; in the third chapter we looked at whether a game with combined keyboard, mouse and BCI input would be favoured against the classical game with just keyboard and mouse. In a large study with 48 participants we found that the participants enjoyed playing the game with BCI control as long as the one without the use of a BCI, while their level of perceived control was significantly lower in the game with the use of BCI. This chapter shows us that by implementing the BCI input in such a way that it’s not detrimental to the UX (i.e. not frustrating or boring) the advantages (i.e. interesting new technology, a different modality to master besides hand-eye coordination) can overcome the disadvantages. In the fourth chapter, we present BrainBrush, a system that takes input from three different modalities: brain activity (by means of the P300 in the EEG), eye blinks (also from the EEG) and head movement (by means of a gyroscope). The experiment that was carried out is of a qualitative nature but also used a well-validated questionnaire for the usability of the system. After two design iterations the final results show that a system including multiple modalities can be engineered which can measure itself with conventional (i.e. not using brain activity) systems. What we see is that these physiological signals can result in lower control for some, with careful design the system can still be useful and provide a good experience. From the results of all these studies we can draw the conclusion that using a BCI as an input channel can make a game or system more challenging and interesting, although it’s far from perfect as a controller. However, care has to be taken when implementing a BCIbased input. By evaluating the UX in user studies we are able to see whether the BCI just frustrates the user or adds an extra dimension