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  Unsupervised Classification for non-invasive Brain-Computer-Interfaces

Eren, S., Grosse-Wentrup, M., & Buss, M. (2007). Unsupervised Classification for non-invasive Brain-Computer-Interfaces. In R. Tita (Ed.), Automatisierungstechnische Verfahren für die Medizin: 7. Workshop (pp. 65-66). Düsseldorf, Germany: VDI Verlag.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CB95-C Version Permalink: http://hdl.handle.net/21.11116/0000-0003-D1FA-7
Genre: Conference Paper

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Automed-Workshop-2007-GrosseWentrup.pdf (Any fulltext), 121KB
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 Creators:
Eren, SE, Author
Grosse-Wentrup, M1, Author              
Buss, M, Author
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1External Organizations, ou_persistent22              

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 Abstract: 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.

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 Dates: 2007-10
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 4985
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Title: Automed Workshop 2007
Place of Event: München, Germany
Start-/End Date: 2007-10-19 - 2007-10-20

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Title: Automatisierungstechnische Verfahren für die Medizin: 7. Workshop
Source Genre: Proceedings
 Creator(s):
Tita, R, Editor
Affiliations:
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Publ. Info: Düsseldorf, Germany : VDI Verlag
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 65 - 66 Identifier: ISBN: 978-3-18-326717-0

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Title: Fortschritt-Berichte VDI ; Reihe 17, Biotechnik/Medizintechnik
Source Genre: Series
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Pages: - Volume / Issue: 267 Sequence Number: - Start / End Page: - Identifier: -