One of the main research interests of hybrid is the design of brain-based interaction in virtual reality. This calls for 3D user interfaces based on brain-computer interfaces, and mind-based control. Hybrid explores its applicability on different use-case scenarios. Here we show some examples:
The MindShooter videogame
Brain-Computer Interfaces became more available for general public and they have been already used to control applications such as computer games. One disadvantage of Brain-Computer Interfaces is that they are not completely reliable. In order to increase BCI performances, some adjustments can be made on low levels, such as signal processing and on high levels – by modifying the controller paradigm. In this study. We explore a novel, context dependent, approach for SSVEP-based BCI controller. This controller uses two kinds of behavior alternation, commands can be added and removed if their use is irrelevant to the context or the actions resulting from their activation can be weighted depending on the likeliness of the actual intention of the user. This controller has been integrated within a BCI computer game and its influence in performance and mental workload has been addressed through a pilot experiment. Preliminary results have shown a workload reduction and performance improvement with the context-dependent controller while keeping the engagement levels untouched.
- Towards Contextual SSVEP-based BCI controller: smart activation of stimuli and controls weighting
- Jozef Legény, Raquel Viciana Abad, Anatole Lécuyer
- IEEE Transactions on Computational Intelligence and AI in games, IEEE Computational Intelligence Society, 2013, 5 (2), pp.111-116
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Multi-User Brain-Computer Interfaces
We created a multi-user video game called “Brain Arena” in which two users can play a simple football game by means of two BCIs. They can score goals on the left or right side of the screen by simply imagining left or right hand movements. To add another interesting element, the gamers can play in a collaborative manner (their two mental activities are combined to score in the same goal), or in a competitive manner (the gamers must push the ball in opposite directions). Two experiments were conducted to evaluate the performance and subjective experience of users in the different conditions. In the first experiment we compared single-user situation with one multiuser situation: the collaborative task. Experiment 1 showed that multi-user conditions are significantly preferred in terms of fun and motivation compared to the single-user condition. The performance of some users was even significantly improved in the multi-user condition. A subset of well-performing subjects was involved in the second experiment, where we added the competitive task. Experiment 2 suggested that competitive and collaborative conditions may lead to similar performances and motivations. However the corresponding gaming experiences can be perceived differently among the participants. Taken together our results suggest that multi-user BCI applications can be operational, effective, and more engaging for participants.
- Two Brains, One Game: Design and Evaluation of a Multi-User BCI Video Game Based on Motor Imagery
- Laurent Bonnet, Fabien Lotte, Anatole Lécuyer
- IEEE Transactions on Computational Intelligence and AI in games, IEEE Computational Intelligence Society, 2013, 5 (2), pp.185-198
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Haptics and BCI
We introduce the combined use of Brain-Computer Interfaces (BCI) and Haptic interfaces. We propose to adapt haptic guides based on the mental activity measured by a BCI system. This novel approach is illustrated within a proof-of-concept system: haptic guides are toggled during a path-following task thanks to a mental workload index provided by a BCI. The aim of this system is to provide haptic assistance only when the user’s brain activity reflects a high mental workload. A user study conducted with 8 participants shows that our proof-of-concept is operational and exploitable. Results show that activation of haptic guides occurs in the most difficult part of the path following task. Moreover it allows to increase task performance by 53% by activating assistance only 59% of the time. Taken together, these results suggest that BCI could be used to determine when the user needs assistance during haptic interaction and to enable haptic guides accordingly.
- Combining Brain-Computer Interfaces and Haptics: Detecting Mental Workload to Adapt Haptic Assistance
- Laurent George, Maud Marchal, Loeïz Glondu, Anatole Lécuyer
- EuroHaptics, Jun 2012, Tampere, Finland. ⟨10.1007/978-3-642-31401-8_12⟩
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