Covert attention as a paradigm for subject-independent brain-computer interfacing
Berlin/Heidelberg : Springer Verlag
InLecture Notes in Computer Science, (2012)Langs, G.; et al. (ed.), Machine Learning and Interpretation in Neuroimaging 2011, pp. 156-163
Article in monograph or in proceedings
Display more detailsDisplay less details
SW OZ DCC AI
SW OZ DCC CO
Lecture Notes in Computer Science
Langs, G.; et al. (ed.), Machine Learning and Interpretation in Neuroimaging 2011
SubjectLecture Notes in Computer Science; Cognitive artificial intelligence; DI-BCB_DCC_Theme 2: Perception, Action and Control; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication
Covert attention has been introduced as a paradigm for gaze-independent brain computer interfacing (BCI). As we know that the applicability of a BCI system depends on the consistency of its paradigm over a group of subjects, one important feature of a BCI is its subject-independence. To generalize a BCI to be applicable for new users, one can think of either to use a machine learning algorithm above a standard BCI to improve the generalization or to choose a more appropriate paradigm. Various machine learning algorithms have been applied on imaginary movement based paradigms towards having a subject-independent BCI, yet there are a few efforts on testing other paradigms in this context. In this paper we show that covert attention can be used as a robust paradigm for subject-independent brain computer interfacing.
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.