A design space for multimodal systems: concurrent processing and data fusion
CHI '93 Proceedings of the INTERACT '93 and CHI '93 Conference on Human Factors in Computing Systems
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards a Better Understanding of Context and Context-Awareness
HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
Challenges in adopting speech recognition
Communications of the ACM - Multimodal interfaces that flex, adapt, and persist
Automatic ranking of information retrieval systems using data fusion
Information Processing and Management: an International Journal
A survey on context-aware systems
International Journal of Ad Hoc and Ubiquitous Computing
Designing for uncertain, asymmetric control: Interaction design for brain-computer interfaces
International Journal of Human-Computer Studies
Human-Computer Interaction for BCI Games: Usability and User Experience
CW '10 Proceedings of the 2010 International Conference on Cyberworlds
A study in user-centered design and evaluation of mental tasks for BCI
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
What We Can Learn from Steve Jobs
IT Professional
New input modalities for modern game design and virtual embodiment
VR '12 Proceedings of the 2012 IEEE Virtual Reality
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Brain computer interfaces as intelligent sensors for enhancing human-computer interaction
Proceedings of the 14th ACM international conference on Multimodal interaction
| Hi-index | 0.00 |
Brain-computer interfaces offer a valuable input modality, which unfortunately comes also with a high degree of uncertainty. There are simple methods to improve detection accuracy after the incoming brain activity has already been classified, which can be divided into (1) gathering additional evidence from other sources of information, and (2) transforming the unstable classification results to be more easy to control. The methods described are easy to implement, but it is essential to apply them in the right way. This paper provides an overview of the different techniques, showing where to apply them and comparing the effects. Detection accuracy is important, but there are trade-offs to consider. Future research should investigate the effectiveness of these methods in their context of use, as well as the optimal settings to obtain the right balance between functionality and meeting the user's expectations for maximum acceptance.