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

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

Passive BCI in operational environments: insights, recent advances and future trends

this mini-review aims to highlight recent important aspects to consider and evaluate when passive Brain-Computer Interface (pBCI) systems would be developed and used in operational environments, and remarks future directions of their applications

Data S1: Data

We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high variability and non-normality of attention and meditation data, which makes each of them difficult to use as an input to control tasks. BCI illiteracy may be a significant problem, as well as setting up of the proper environment of the experiment. The results of blinking recognition show that using the Neurosky device means recognition accuracy is less than 50%, while the Emotiv device has achieved a recognition accuracy of more than 75%; for tasks that require concentration and relaxation of subjects, the Emotiv EPOC device has performed better (as measured by the recognition accuracy) by ∼9%. Therefore, the Emotiv EPOC device may be more suitable for control tasks using the attention/meditation level or eye blinking than the Neurosky MindWave device

Games and Brain-Computer Interfaces: The State of the Art

BCI gaming is a very young field; most games are proof-of-concepts. Work that compares BCIs in a game environments with traditional BCIs indicates no negative effects, or even a positive effect of the rich visual environments on the performance. The low transfer-rate of current games poses a problem for control of a game. This is often solved by changing the goal of the game. Multi-modal input with BCI forms an promising solution, as does assigning more meaningful functionality to BCI control

Single trial classification of EEG in predicting intention and direction of wrist movement : translation toward development of four-class brain computer interface system based on a single limb

Brain - computer interfaces (BCI) are paradigms that offer an alternative communication channel between neural activity gene rated in the brain and the users’ external environment. The aim of this paper is to investigate the feasibility of designing and developing a multiclass BCI system based on a single limb movement due to the factor, high dimensional control channels would expand the capacity of BCI application (multidimensional control of neuroprosthesis). This paper also proposes a method to identify the optimal frequency band and recording channel to achieve the best classification result . Twenty eight surface electroencephalography ( EEG ) electrodes are used to record brain activity from eleven subjects whilst imagining and performing right wrist burst point - to - point movement towards multiple directions using a high density montage with 10 - 10 electrode placement locations focusing on motor cortex areas. Two types of spatial filters namely Common average reference (CAR) and Laplacian (LAP) filter have been implemented and results are compared to enhance the EEG signal. Features are extracted from the filtered signals using event related spectral perturbation ( ERSP ) and power spectrum. Feature vectors are classified by k - nearest neighbour ( k - NN) and quadratic discriminant analysis (QDA) classifiers. The results indicate that the majority of the optimum classification results are obtained from features extracted from contralateral electrodes in the gamma band. Based on a single trial, the average of the classification accuracy using LAP filter and k - NN classifier across the subjects in predicting intention and direction of movement is 68% and 62% for motor imagery and motor performance respectively; which is significantly higher than chance. The classification result from the majority of subjects shows that, it is possible and achievable to develop multiclass BCI systems based on a single limb