Amorim, Renato and Mirkin, Boris and Gan, John Q (2012) Anomalous pattern based clustering of mental tasks with subject independent learning – some preliminary results. Artificial Intelligence Research, 1 (1). p. 55. DOI https://doi.org/10.5430/air.v1n1p55
Amorim, Renato and Mirkin, Boris and Gan, John Q (2012) Anomalous pattern based clustering of mental tasks with subject independent learning – some preliminary results. Artificial Intelligence Research, 1 (1). p. 55. DOI https://doi.org/10.5430/air.v1n1p55
Amorim, Renato and Mirkin, Boris and Gan, John Q (2012) Anomalous pattern based clustering of mental tasks with subject independent learning – some preliminary results. Artificial Intelligence Research, 1 (1). p. 55. DOI https://doi.org/10.5430/air.v1n1p55
Abstract
<jats:p>In this paper we describe a new method for EEG signal classification in which the classification of one subject’s EEG signals is based on features learnt from another subject. This method applies to the power spectrum density data and assigns class-dependent information weights to individual features. The informative features appear to be rather similar among different subjects, thus supporting the view that there are subject independent general brain patterns for the same mental task. Classification is done via clustering using the intelligent k-means algorithm with the most informative features from a different subject. We experimentally compare our method with others.</jats:p>
Item Type: | Article |
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Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 22 Apr 2013 14:05 |
Last Modified: | 23 Sep 2022 19:15 |
URI: | http://repository.essex.ac.uk/id/eprint/6013 |
Available files
Filename: AIR2012.pdf
Licence: Creative Commons: Attribution 3.0