Brain-computer interfaces for 3D games

Lotte, Fabien

doi:10.1145/2159365.2159427

Cited by 34 publications

(16 citation statements)

References 19 publications

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“…For all those reasons today, BCI hardly compete with traditional game effectors such as joysticks, mice, and keyboards, at least in healthy subjects. However, unreliable input control could be used to extend current video games and to create a motivating challenge for the users [19].…”

Section: Introductionmentioning

confidence: 99%

BCI Could Make Old Two-Player Games Even More Fun: A Proof of Concept with “Connect Four”

Maby

Perrin

Bertrand

et al. 2012

Advances in Human-Computer Interaction

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We present a brain-computer interface (BCI) version of the famous "Connect Four". Target selection is based on brain event-related responses measured with nine EEG sensors. Two players compete against each other using their brain activity only. Importantly, we turned the general difficulty of producing a reliable BCI command into an advantage, by extending the game play and rules, in a way that adds fun to the game and might well prove to trigger up motivation in future studies. The principle of this new BCI is directly inspired from our own implementation of the classical P300 Speller (Maby et al. 2010. We here establish a proof of principle that the same electrophysiological markers can be used to design an efficient two-player game. Experimental evaluation on two competing healthy subjects yielded an average accuracy of 82%, which is in line with our previous results on many participants and demonstrates that the BCI "Connect Four" can effectively be controlled. Interestingly, the duration of the game is not significantly affected by the usual slowness of BCI commands. This suggests that this kind of BCI games could be of interest to healthy players as well as to disabled people who cannot play with classical games.

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Section: Introductionmentioning

confidence: 99%

BCI Could Make Old Two-Player Games Even More Fun: A Proof of Concept with “Connect Four”

Maby

Perrin

Bertrand

et al. 2012

Advances in Human-Computer Interaction

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“…The difficulty was that the user had to learn how to control his/her brain activity; the machine did not adapt to the user's neural signal. Today 90 years after Berger's first experiments EEG-based BCI systems have been successfully used for various applications (see Dunne et al, 2013), e.g., communication (Farwell and Donchin, 1988;Birbaumer et al, 1999;Nijboer et al, 2008;Treder and Blankertz, 2010), neurological rehabilitation (Birbaumer and Cohen, 2007;Daly and Wolpaw, 2008;Kaiser et al, 2011;Ang et al, 2011;Lim et al, 2012;Ang and Guan, 2013;Courtine et al, 2013), wheelchair control (Leeb et al, 2007;Galán et al, 2008;Rebsamen et al, 2010;del R. Millán et al, 2010;Carlson and del R. Millán, 2013), prosthesis control (Guger et al, 1999;Müller-Putz et al, 2005;Jackson et al, 2006;McFarland and Wolpaw, 2008;Hahne et al, 2012), game playing (Lalor et al, 2005;Nijholt and Tan, 2007;Tangermann et al, 2008;Nijholt et al, 2009;Lotte, 2011;Bonnet et al, 2013) and many additional non-medical applications del R. Millán et al, 2009;Blankertz et al, 2010b;Haufe et al, 2011;van Erp et al, 2012;Porbadnigk et al, 2013).…”

Section: Part I R E V I S I T I N G B C Imentioning

confidence: 99%

On robust spatial filtering of EEG in nonstationary environments

Samek

2016

It - Information Technology

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bibliography 119 I N T R O D U C T I O NVariability is the law of life, and as no two faces are the same, so no two bodies are alike, and no two individuals react alike and behave alike under the abnormal conditions which we know as disease. (Sir William Osler, 1903) T he Canadian physician Sir William Osler (1849 -1919 revolutionized the medical world of the 19th century by advocating an approach to therapy which focuses on the needs of individual patients. He recognized the great variability among individuals with respect to their physical conditions, their mental status and their responses to drugs and concluded that focusing solely on clinical signs and symptoms does not result in the most effective therapy. The development of this novel patient-centered paradigm made him the father of personalized medicine; a branch of medicine that is also very popular today due to a better understanding of the genetic influences on a person's risk of acquiring certain diseases.In the last two decades personalized approaches have also revolutionized the field of Brain-Computer Interfacing (BCI). A BCI system (see e.g. Wolpaw et al., 2002;Dornhege et al., 2007;Wolpaw and Wolpaw, 2012) aims to decode the intention of a user from recordings of brain activity and to use this information for controlling a computer application or a robotic device. Today's BCIs extract user-specific features from electroencelographic (EEG) recordings and adapt to the user's signal characteristics. This subject-centered or machine learning approach not only largely reduces the calibration time in comparison to classical BCI systems based on neurofeedback training (Kamiya et al., 1969) but also increases classification accuracy. Unfortunately, EEG responses to a stimulus or a task not only differ from subject to subject but also from trial to trial and from day to day. This change in the signal properties over time is termed the intrinsic nonstationarity of EEG (Kaplan et al., 2005) and constitutes a major challenge for data analysis and classification by violating a basic assumption of many machine learning methods, namely that data are sampled from a fixed (but unknown) distribution (Vapnik, 1998;Hastie et al., 2001). The analysis of EEG is further aggravated by the presence of artifacts in the data, e.g., eye movements, muscular activity or loose electrodes. The lack of robustness to unexpected events and the nonstationary nature of EEG are major reasons in explaining why current BCI technology is seldom used in out-of-lab scenarios and clinical practice. Since artifacts and nonstationarity can not be fully eliminated, even with the best experimental protocol, robust and invariant feature 1 2 introduction representations are required for optimal signal analysis and high classification accuracy. structure of the thesisThis thesis is divided into three parts. The first part introduces the basic concepts of motor imagery-based Brain-Computer Interfacing and discusses the advantages and limitations of several state-of-the-art spatial filtering al...

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“…In a current survey, [20] point out the potential of increasing general interaction quality through adapting interactive systems based on user BCI data. Video games in particular are expected to benefit from such adaptation [31]. Similar approaches and analyses exist for data derived via eye movement tracking instead of BCI interfaces, including for video game control [39] or for context-aware games [9].…”

Section: Introductionmentioning

confidence: 99%

Dynamically adapting an AI game engine based on players' eye movements and strategies

Wetzel

Spiel

Bertel

2014

Proceedings of the 2014 ACM SIGCHI Symposium on Engineering Interactive Computing Systems

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Artificial intelligence (AI) game engines have frequently been used to drive computational antagonists when playing games against humans. Limited work exists, however, on using human players' psychophysical measures to directly parametrise AI game engines. Instead, parameters to optimise AI performance are usually derived from general play-related data or user models. This paper presents novel research on using eye movement data in addition to data on users' strategies to adapt the live play of a computational antagonist in the visuo-spatial strategy game, Hex. It offers a set of suitable parameters for both types of data. A systematic evaluation of the approach showed, among other things, that using eye movement data led to significantly better gameplay experience for human players, as they experienced less frustration with sufficient challenge. Findings are discussed not only with regard to designing gameplay experience, but also their more general ramifications on using live psychophysical data for intelligent interactive systems.

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Brain-computer interfaces for 3D games

Cited by 34 publications

References 19 publications

BCI Could Make Old Two-Player Games Even More Fun: A Proof of Concept with “Connect Four”

BCI Could Make Old Two-Player Games Even More Fun: A Proof of Concept with “Connect Four”

On robust spatial filtering of EEG in nonstationary environments

Dynamically adapting an AI game engine based on players' eye movements and strategies

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