Abstract:
:In this work the application of different machine learning techniques for classification of mental tasks from electroencephalograph (EEG) signals is investigated. The main application for this research is the improvement of brain computer interface (BCI) systems. For this purpose, Bayesian graphical network, neural network, Bayesian quadratic, Fisher linear and hidden Markov model classifiers are applied to two known EEG datasets in the BCI field. The Bayesian network classifier is used for the first time in this work for classification of EEG signals. The Bayesian network appeared to have a significant accuracy and more consistent classification compared to the other four methods. In addition to classical correct classification accuracy criteria, the mutual information is also used to compare the classification results with other BCI groups.
journal_name
J Neural Engjournal_title
Journal of neural engineeringauthors
Rezaei S,Tavakolian K,Nasrabadi AM,Setarehdan SKdoi
10.1088/1741-2560/3/2/008subject
Has Abstractpub_date
2006-06-01 00:00:00pages
139-44issue
2eissn
1741-2560issn
1741-2552pii
S1741-2560(06)18739-Xjournal_volume
3pub_type
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更新日期:2009-06-01 00:00:00
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更新日期:2018-04-01 00:00:00
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更新日期:2018-08-01 00:00:00
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