Comparison of Five Different Classifiers for Classification of Mental Tasks

Author

Rezaei, Siamak ; Tavakolian, Kouhyar ; Naziripour, Kiarash

Author_Institution

Comput. Sci., University of Northern British Columbia, BC

fYear

2006

Firstpage

6007

Lastpage

6010

Abstract

In this work we consider classification of mental tasks from EEG signal by using five different classifiers. These include neural network, Bayesian graphical network, Bayesian quadratic classifier, hidden Markov model and Fisher linear classifier. The experimental results are compared to each other. The Bayesian network appears to have comparable accuracy with the best classifier of the five but the classification time for it is more than others

Keywords

Bayes methods; electroencephalography; hidden Markov models; medical signal processing; neural nets; signal classification; Bayesian graphical network; Bayesian quadratic classifier; EEG signal; Fisher linear classifier; hidden Markov model; mental task classification; neural network; Bayesian methods; Biological neural networks; Biomedical computing; Brain computer interfaces; Brain modeling; Electroencephalography; Feature extraction; Hidden Markov models; Iterative algorithms; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

Conference_Location

Shanghai

Print_ISBN

0-7803-8741-4

Type

conf

DOI

10.1109/IEMBS.2005.1615860

Filename

1615860

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1585627