Hilfe Wegweiser Impressum Kontakt Einloggen





Unsupervised Classification for non-invasive Brain-Computer-Interfaces


Grosse-Wentrup,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar

Eren, S., Grosse-Wentrup, M., & Buss, M. (2007). Unsupervised Classification for non-invasive Brain-Computer-Interfaces. Automed Workshop 2007, 65-66.

Non-invasive Brain-Computer-Interfaces (BCIs) are devices that infer the intention of human subjects from signals generated by the central nervous system and recorded outside the skull, e.g., by electroencephalography (EEG). They can be used to enable basic communication for patients who are not able to communicate by normal means, e.g., due to neuro-degenerative diseases such as amyotrophic lateral sclerosis (ALS) (see [Vaughan2003] for a review). One challenge in research on BCIs is minimizing the training time prior to usage of the BCI. Since EEG patterns vary across subjects, it is usually necessary to record a number of trials in which the intention of the user is known to train a classifier. This classifier is subsequently used to infer the intention of the BCI-user. In this paper, we present the application of an unsupervised classification method to a binary noninvasive BCI based on motor imagery. The result is a BCI that does not require any training, since the mapping from EEG pattern changes to the intention of the user is learned online by the BCI without any feedback. We present experimental results from six healthy subjects, three of which display classification errors below 15. We conclude that unsupervised BCIs are a viable option, but not yet as reliable as supervised BCIs. The rest of this paper is organized as follows. In the Methods section, we first introduce the experimental paradigm. This is followed by a description of the methods used for spatial filtering, feature extraction, and unsupervised classification. We then present the experimental results, and conclude the paper with a brief discussion.