The CoDIS Taxonomy for Brain-Computer Interface Games Controlled by Electroencephalography

Abstract

Brain-computer interfaces (BCI) are systems able to respond to a user’s intent based on physiological information acquired directly from his/her brain. BCI-based games employ those physiological data to control or alter the game world, providing not only the possibility of greater immersion for the player, but also allowing virtually any person, regardless of physical disabilities, to play the game, allowing many research possibilities for the field of Human-Computer Interaction (HCI). However, being an interdisciplinary field, research with BCI-based games is often conducted by researchers from different areas, making it difficult to organize and compare studies involving those games. In this context, this article presents a model-based taxonomy for games controlled by electroencephalography (EEG), a specific case of non-invasive modality for BCI systems. The taxonomy, called CoDIS, was constructed after surveying the literature and considering four aspects of EEG-controlled games, i.e., Concept, Design, Implementation and Study, each with different dimensions and attributes to represent a total of 48 characteristics of such games. Each of these dimensions was then described considering the current state of the field, and the usefulness of the taxonomy was demonstrated based on examples from the literature. The taxonomy may be used by researchers, designers and developers to classify, identify, implement and compare EEG-based games based on the dimensions of the taxonomy.

Keywords:

• BCI
• EEG
• HCI
• game
• taxonomy

Acknowledgements

This work was supported by the Physical Artifacts of Interaction Research Group (PAIRG) at the Federal University of Rio Grande do Norte (UFRN). We thank the resources of the PAIRG’s Laboratory of Physical and Physiological Computing (PAIRG L2PC) at UFRN.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Some authors also consider a distinction between” collaborative” and” cooperative” approaches. While the first refers to a coordinated effort to solve the task simultaneously, the latter refers to dividing the task into smaller sub-tasks, and each person or group is responsible for a given task.

2 https://unity.com

3 https://www.unrealengine.com.

4 https://sccn.ucsd.edu/eeglab.

5 https://www.bci2000.org.

6 http://openvibe.inria.fr/.

7 https://www.mathworks.com/products/matlab.html.

8 http://www.gtec.at/Products/Hardware-and-Accessories/g.USBamp-Specs-Features.

9 http://www.neurobitsystems.com/neurobitoptima.htm.

Additional information

Funding

This work was partially funded by the Brazilian Federal Agency for Support and Evaluation of Graduate Education [CAPES grant #88882.375573/2019-01].

Notes on contributors

Gabriel Alves Mendes Vasiljevic

Gabriel Alves Mendes Vasiljevic is an Assistant Professor at the Federal University of Rio Grande do Norte (UFRN) and a researcher of the Physical Artifacts of Interaction Research Group (PAIRG). He received a PhD in Computer Science from UFRN. He is currently working on researches focused on brain-computer interfaces.

Leonardo Cunha de Miranda

Leonardo Cunha de Miranda is an Associate Professor at the Federal University of Rio Grande do Norte (UFRN). He received a PhD in Computer Science from University of Campinas (UNICAMP). He is the founder-head of the Physical Artifacts of Interaction Research Group (PAIRG) at UFRN.