Using Selforganizing Feature Maps to Classify EEG Coherence Maps View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1993

AUTHORS

Georg Dorffner , Peter Rappelsberger , Arthur Flexer

ABSTRACT

In this work we have been applying self-organizing feature maps [3] to the problem of unsupervised classification of EEG data. The type of EEG used are so-called coherence maps based on 19 electrodes, which were derived during specific cognitive taks such as mental rotation. The goal was to exploit the network learning scheme as extractor for any task- (or other parameter-) related information in the data. In other words, we used the self-organizing feature maps to detect whether the EEG inputs can be classified acording to underlying parameters such as the type of task performed. This paper reports about the very promising results of the experiments. More... »

PAGES

882-887

References to SciGraph publications

Book

TITLE

ICANN ’93

ISBN

978-3-540-19839-0
978-1-4471-2063-6

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4471-2063-6_258

DOI

http://dx.doi.org/10.1007/978-1-4471-2063-6_258

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009999732


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