Turning shortcomings into challenges: Brain–computer interfaces for games

Abstract

Abstract. In recent years we have seen a rising interest in brain-computer interfacing for human-computer interaction and potential game applications. Until now, however, we have almost only seen attempts where BCI is used to measure the affective state of the user or in neurofeedback games. There have hardly been any attempts to design BCI games where BCI is considered to be one of the possible input modalities that can be used to control the game. One reason may be that research still follows the paradigms of the traditional, medically oriented, BCI approaches. In this paper we discuss current BCI research from the viewpoint of games and game design. It is hoped that this survey will make clear that we need to design different games than we used to, but that such games can nevertheless be interesting and exciting.

Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design

“…Results suggested that users can deal with a multi-dimensional feedback without decrease in performance, although neither without significant increase in performance here. Using BCI with game-like, 3D or Virtual Reality (VR) feedback environments have also been shown to increase BCI performances (Leeb et al, 2006; Lécuyer et al, 2008; Nijholt et al, 2009; Ron-Angevin and Diaz-Estrella, 2009; Lotte et al, 2013). In the same vein, feedback from multiple users playing a BCI-based game together has been shown to increase BCI performances as compared to feedback provided from the user only, during a single-player version of the same game (Bonnet et al, 2013).…”

Performance of the Emotiv Epoc headset for P300-based applications

“…Other main research areas are the study of motor substitution or motor rehabilitation whose main applications are hand grasping [2] and wheelchair control [3]. Finally, for healthy end-users, BCIs have also been used to augment interactivity in games by using multimodality from the Electroencephalography (EEG) signals and the standard control [4,5]. …”

Brainatwork

“…Prior research [15] provided a formula to calculate cognitive engagement using the α(7−11Hz), β(11−20Hz), and θ(4 − 7Hz) frequency bands, where E, representing engagement, is calculated as: E = β α+θ (1). The EEG Engagement index reflects visual processing and sustained attention [3] and can identify changes in attention related to stimuli due 1 http://neurosky.com to its high temporal resolution [3,14]. This equation was successfully utilized in conjunction with the Neurosky Mindwave to calculate cognitive engagement [8,16,17].…”

“…In specific, we utilize Electroencephalography (EEG) signals from the frontal lobe of the brain which are able to detect shifts in engagement and workload [3] and map them to workplace activities logged through a PC logger. Properties of EEG signals such as the different frequency bands provide cognitive information with a high temporal resolution that can be related to real-world stimuli [3,14]. HCI research recently showed the feasibility of using consumer EEG sensors for sensing user's engagement in several domains [1,8,9,16,17,18].…”