Bram van de Laar

Brain-Computer Interfacing and Games

Recently research into Brain-Computer Interfacing (BCI) applications for healthy users, such as games, has been initiated. But why would a healthy person use a still-unproven technology such as BCI for game interaction? BCI provides a combination of information and features that no other input modality can offer. But for general acceptance of this technology, usability and user experience will need to be taken into account when designing such systems. Therefore, this chapter gives an overview of the state of the art of BCI in games and discusses the consequences of applying knowledge from Human-Computer Interaction (HCI) to the design of BCI for games. The integration of HCI with BCI is illustrated by research examples and showcases, intended to take this promising technology out of the lab. Future research needs to move beyond feasibility tests, to prove that BCI is also applicable in realistic, real-world settings.

How Much Control Is Enough? Influence of Unreliable Input on User Experience

Brain-computer interfaces (BCI) provide a valuable new input modality within human-computer interaction systems. However, like other body-based inputs such as gesture or gaze based systems, the system recognition of input commands is still far from perfect. This raises important questions, such as what level of control should such an interface be able to provide. What is the relationship between actual and perceived control? And in the case of applications for entertainment in which fun is an important part of user experience, should we even aim for the highest level of control, or is the optimum elsewhere? In this paper, we evaluate whether we can modulate the amount of control and if a game can be fun with less than perfect control. In the experiment users (n=158) played a simple game in which a hamster has to be guided to the exit of a maze. The amount of control the user has over the hamster is varied. The variation of control through confusion matrices makes it possible to simulate the experience of using a BCI, while using the traditional keyboard for input. After each session the user completed a short questionnaire on user experience and perceived control. Analysis of the data showed that the perceived control of the user could largely be explained by the amount of control in the respective session. As expected, user frustration decreases with increasing control. Moreover, the results indicate that the relation between fun and control is not linear. Although at lower levels of control fun does increase with improved control, the level of fun drops just before perfect control is reached (with an optimum around 96%). This poses new insights for developers of games who want to incorporate some form of BCI or other modality with unreliable input in their game: for creating a fun game, unreliable input can be used to create a challenge for the user.

Usability of Three Electroencephalogram Headsets for Brain–Computer Interfaces: A Within Subject Comparison

Currently the field of brain–computer interfacing is increasingly focused on developing usable brain–computer interfaces (BCIs) to better ensure technology transfer and acceptance. Many studies have investigated the usability of BCI applications as a whole. Here we aim to investigate one specific component of an electroencephalogram (EEG)-based BCI system: the acquisition component. This study compares on the usability of three different EEG headsets in the context of a P300-based BCI application for communication. Thirteen participants took part in a within-subject experiment. Participants were randomly given a Biosemi, Emotiv EPOC or g.Sahara headset. After every session offline classification accuracy (efficacy) was calculated and usability factors (perceived efficiency and user satisfaction) were measured using questionnaires. The 32-channel Biosemi headset offered the highest accuracy (88.5%) compared with the 8-channel g.Sahara (62.7%) and the 14-channel Emotiv (61.7%). There was no difference in accuracy between the Biosemi and the g.Sahara when comparing the same 8 channels. The Biosemi and g.Sahara were rated as more comfortable than the Emotiv. The Emotiv was rated as best for aesthetics. System setup time was highest for the Biosemi headset when compared with the g.Sahara and the Emotiv. Without information about the effectiveness, participants preferred the Emotiv. We recommend the use of a gelled headset for applications which require high accuracy and efficiency and water-based or dry headsets when aesthetics, easy setup and fun are important.

Human-Computer Interaction for BCI Games: Usability and User Experience

Brain-computer interfaces (BCI) come with a lot of issues, such as delays, bad recognition, long training times, and cumbersome hardware. Gamers are a large potential target group for this new interaction modality, but why would healthy subjects want to use it? BCI provides a combination of information and features that no other input modality can offer. But for general acceptance of this technology, usability and user experience will need to be taken into account when designing such systems. This paper discusses the consequences of applying knowledge from Human-Computer Interaction (HCI) to the design of BCI for games. The integration of HCI with BCI is illustrated by research examples and showcases, intended to take this promising technology out of the lab. Future research needs to move beyond feasibility tests, to prove that BCI is also applicable in realistic, real-world settings.

Evaluating User Experience of Actual and Imagined Movement in BCI Gaming

Most research on Brain-Computer Interfaces BCI focuses on developing ways of expression for disabled people who are not able to communicate through other means. Recently it has been shown that BCI can also be used in games to give users a richer experience and new ways to interact with a computer or game console. This paper describes research conducted to find out what the differences are between using actual and imagined movement as modalities in a BCI game. Results show that there are significant differences in user experience and that actual movement is a more robust way of communicating through a BCI.

Perspectives on user experience evaluation of brain-computer interfaces

The research on brain-computer interfaces (BCIs) is pushing hard to bring technologies out of the lab and into society and onto the market. The nascent merge between the field of BCI and humancomputer interaction (HCI) is paving the way for new applications such as BCI-controlled gaming. The evaluation or success of BCI technologies is often based on how accurate the control of a user is with the technology. However, while this is still key to its usability, other factors that influence the user experience (UX) can make or break a technology. In this paper we first review studies which investigated user experience with BCIs. Second, we will discuss how HCI approaches can contribute to the evaluation of BCIs. Finally, we propose to develop a standardized questionnaire for evaluating BCIs for entertainment purposes.

Brain–Computer Interfaces and User Experience Evaluation

The research on brain–computer interfaces (BCIs) is pushing hard to bring technologies out of the lab, into society and onto the market. The newly developing merge of the field of BCI with human–computer interaction (HCI) is paving the way for new applications such as BCI-controlled games. The evaluation or success of BCI technologies is often based on how accurate the control of a user is over the technology. However, while this is still key to its usability, other factors that influence the user experience (UX) can make or break a technology. In this paper we first review studies that investigated user experience with BCIs. Second, we will discuss how methods from the field of HCI can contribute to the evaluation of BCIs. From experience drawn from two case studies we provide recommendations for evaluating BCIs.

Looking around with your brain in a virtual world

Offline analysis pipelines have been developed and evaluated for the detection of covert attention from electroen-cephalography recordings, and the detection of overt attention in terms of eye movement based on electrooculographic measurements. Some additional analysis were done in order to prepare the pipelines for use in a real-time system. This real-time system and a game application in which these pipelines are to be used were implemented. The game is set in a virtual environment where player is a wildlife photographer on an uninhabited island. Overt attention is used to adjust the angle of the first person camera, when the player is tracking animals. When making a photograph, the animal will flee when it notices it is looked at directly, so covert attention is required to get a good shot. Future work will entail user tests with this system to evaluate usability, user experience, and characteristics of the signals related to overt and covert attention when used in such an immersive environment.

Perception and Manipulation of Game Control

Brain-computer interfaces do not provide perfect recognition of user input, for similar reasons as natural input modalities. How well can users assess the amount of control they have, and how much control do they need? We describe an experiment where we manipulated the control users had in a keyboard-controlled browser game. The data of 211 runs from 87 individuals indicates a significant linear correlation between users’ sense of control and the amount of control they really had in terms of mutual information (not accuracy!). If users know what they put in, they can assess quite well how much control they have over the system. In our case, from an amount of control of above 0.68 bits in mutual information (a 5-class accuracy of 65%), this aspect of control no longer seems to be the critical factor for finishing the game. Deliberate manipulation of perception may offer a way to make imperfect, uncertain input modalities more acceptable, especially in combination with games.

Developing educational and entertaining virtual humans using Elckerlyc

Virtual humans (VHs) are used in many educational and entertainment settings: training and serious gaming, interactive information kiosks, tour guides, tutoring, interactive virtual dancers, and much more. Building a complete VH from scratch is a daunting task, and it makes sense to rely on existing platforms. However, when one builds a novel interactive VH application, one needs to be able to adapt and extend the means to control the VH offered by the platform, without reprogramming parts of the platform. This paper describes Elckerlyc, a novel platform for controlling a VH. The focus is on how to easily extend and adapt the system to the needs of a particular application, without programming.