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Context-aware brain-computer interfaces: exploring the information space of user, technical system and environment




Brain–computer interface (BCI) systems are usually applied in highly controlled environments such as research laboratories or clinical setups. However, many BCI-based applications are implemented in more complex environments. For example, patients might want to use a BCI system at home, and users without disabilities could benefit from BCI systems in special working environments. In these contexts, it might be more difficult to reliably infer information about brain activity, because many intervening factors add up and disturb the BCI feature space. One solution for this problem would be adding context awareness to the system. We propose to augment the available information space with additional channels carrying information about the user state, the environment and the technical system. In particular, passive BCI systems seem to be capable of adding highly relevant context information—otherwise covert aspects of user state. In this paper, we present a theoretical framework based on general human–machine system research for adding context awareness to a BCI system. Building on that, we present results from a study on a passive BCI, which allows access to the covert aspect of user state related to the perceived loss of control. This study is a proof of concept and demonstrates that context awareness could beneficially be implemented in and combined with a BCI system or a general human–machine system. The EEG data from this experiment are available for public download at www.phypa.org.

Author(s): Zander, TO. and Jatzev, S.
Journal: Journal of Neural Engineering
Volume: 9
Number (issue): 1
Pages: 016003, 10
Year: 2012
Month: February
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1088/1741-2560/9/1/016003

Links: PDF


  title = {Context-aware brain-computer interfaces: exploring the information space of user, technical system and environment},
  author = {Zander, TO. and Jatzev, S.},
  journal = {Journal of Neural Engineering},
  volume = {9},
  number = {1},
  pages = {016003, 10},
  month = feb,
  year = {2012},
  month_numeric = {2}