BrainBasher: a BCI Game

Brain-computer interaction (BCI) is starting to focus on healthy subjects. This research adresses the effects of using this novel input modality to control a simple game, and also looks into the beneficial effects of bringing game elements into BCI experiments. A simple BCI game has been developed and evaluated with fifteen subjects using the Game Experience Questionnaire (GEQ) developed at the Eindhoven Game Experience Lab. Three variations of the game were evaluated for comparison: the original game with BCI input, one with keyboard input, and one with a more clinical look leaving out all extraneous information. The keyboard-controlled game was considered easy and boring, whereas using BCI for input resulted in a more challenging, immersive and richer experience. The design and additional information presented by the game also resulted in higher immersion compared to the clinical design

Fusion for Audio-Visual Laughter Detection

Laughter is a highly variable signal, and can express a spectrum of emotions. This makes the automatic detection of laughter a challenging but interesting task. We perform automatic laughter detection using audio-visual data from the AMI Meeting Corpus. Audio-visual laughter detection is performed by combining (fusing) the results of a separate audio and video classifier on the decision level. The video-classifier uses features based on the principal components of 20 tracked facial points, for audio we use the commonly used PLP and RASTA-PLP features. Our results indicate that RASTA-PLP features outperform PLP features for laughter detection in audio. We compared hidden Markov models (HMMs), Gaussian mixture models (GMMs) and support vector machines (SVM) based classifiers, and found that RASTA-PLP combined with a GMM resulted in the best performance for the audio modality. The video features classified using a SVM resulted in the best single-modality performance. Fusion on the decision-level resulted in laughter detection with a significantly better performance than single-modality classification

Robustness of the Common Spatial Patterns algorithm in the BCI-pipeline

When we want to use brain-computer interfaces (BCI) as an input modality for gaming, a short setup procedure is necessary. Therefore a user model has to be learned using small training sets. The common spatial patterns (CSP) algorithm is often used in BCI. In this work we investigate how the CSP algorithm generalizes when using small training sets, how the performance changes over time, and how well CSP generalizes over persons. Our results indicate that the CSP algorithm severely overfits on small training sets. The CSP algorithm often selects a small number of spatial filters that generalize poorly, which can have in impact on the classification performance. The generalization performance does not degrade over time, which is promising, but the signal does not seem to be stationary. In its current form, the CSP generalizes poorly over persons

Actual and Imagined Movement in BCI Gaming

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

How much control is enough? Optimizing fun with unreliable input

Brain-computer interfaces (BCI) provide a valuable new input modality within human- computer interaction systems, but like other body-based inputs, the system recognition of input commands is 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 perfect control, or is the optimum elsewhere? In this experiment the user plays a simple game in which a hamster has to be guided to the exit of a maze, in which 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 �lled out a short questionnaire on fun 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 in the beginning fun does increase with improved control, the level of fun drops again just before perfect control is reached. This poses new insights for developers of games wanting to incorporate some form of BCI in their game: for creating a fun game, unreliable input can be used to create a challenge for the user

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

Subjective and objective measures

One of the greatest challenges in the study of emotions and emotional states is their measurement. The techniques used to measure emotions depend essentially on the authors’ definition of the concept of emotion. Currently, two types of measures are used: subjective and objective. While subjective measures focus on assessing the conscious recognition of one’s own emotions, objective measures allow researchers to quantify and assess the conscious and unconscious emotional processes. In this sense, when the objective is to evaluate the emotional experience from the subjective point of view of an individual in relation to a given event, then subjective measures such as self-report should be used. In addition to this, when the objective is to evaluate the emotional experience at the most unconscious level of processes such as the physiological response, objective measures should be used. There are no better or worse measures, only measures that allow access to the same phenomenon from different points of view. The chapter’s main objective is to make a survey of the main measures of evaluation of the emotions and emotional states more relevant in the current scientific panorama.info:eu-repo/semantics/acceptedVersio

The future in brain/neural computer interaction: Horizon 2020

The main objective of this roadmap is to provide a global perspective on the BCI field now and in the future. For readers not familiar with BCIs, we introduce basic terminology and concepts. We discuss what BCIs are, what BCIs can do, and who can benefit from BCIs. We illustrate our arguments with use cases to support the main messages. After reading this roadmap you will have a clear picture of the potential benefits and challenges of BCIs, the steps necessary to bridge the gap between current and future applications, and the potential impact of BCIs on society in the next decade and beyond