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| Research article summary (published 30 Jan 2002): |
Relevant EEG features for the classification of spontaneous motor-related tasks.
Full Abstract
There is a growing interest in the use of physiological signals for communication and operation of devices for the severely motor disabled as well as for healthy people. A few groups around the world have developed brain-computer interfaces (BCIs) that rely upon the recognition of motor-related tasks (i.e., imagination of movements) from on-line EEG signals. In this paper we seek to find and analyze the set of relevant EEG features that best differentiate spontaneous motor-related mental tasks from each other. This study empirically demonstrates the benefits of heuristic feature selection methods for EEG-based classification of mental tasks. In particular, it is shown that the classifier performance improves for all the considered subjects with only a small proportion of features. Thus, the use of just those relevant features increases the efficiency of the brain interfaces and, most importantly, enables a greater level of adaptation of the personal BCI to the individual user.
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Author information
Author/s: Millán, JosédelR (J); Franzé, Marco (M); Mouriño, Josep (J); Cincotti, Febo (F); Babiloni, Fabio (F);
Affiliation: ISIS, Joint Research Centre of the EC, Ispra, Italy. jose.millan(-atsign-)jrc.it
Journal and publication information
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal: Biological cybernetics (Biol Cybern), published in Germany. (Language: eng)
Reference: 2002-Feb; vol 86 (issue 2) : pp 89-95
Dates: Created 2002/03/22; Completed 2002/09/03; Revised 2006/11/15;
PMID: 11908842, status: MEDLINE (last retrieval date: 11/6/2008)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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