Classification of mental tasks using Gaussian mixture Bayesian network classifiers

Author

Tavakolian, Kouhyar ; Rezaei, Saeid

Author_Institution

Dept. of Comput. Sci., Northern British Columbia Univ., USA

fYear

2004

fDate

1-3 Dec. 2004

Lastpage

42624

Abstract

In this work we consider classification of mental tasks from EEG signals by using Gaussian mixture models. For this purpose, we use Bayesian graphical networks (BNT). The final results for Bayesian graphical networks are compared with our previous results for the neural network classifier. The results show an improvement in both classification accuracy and consistency.

Keywords

Gaussian processes; belief networks; electroencephalography; medical signal processing; signal classification; Bayesian graphical networks; Bayesian network classifiers; EEG; Gaussian mixture model; mental task classification; Bayesian methods; Biological neural networks; Brain computer interfaces; Brain modeling; Electroencephalography; Graphical models; Iterative algorithms; Mathematical model; Neural networks; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Circuits and Systems, 2004 IEEE International Workshop on

Print_ISBN

0-7803-8665-5

Type

conf

DOI

10.1109/BIOCAS.2004.1454169

Filename

1454169

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3427985