Classification of mental tasks using gaussian mixture bayesian network classifiers

Tavakolian, Kouhyar; Rezaei, Siamak

doi:10.1109/biocas.2004.1454169

Cited by 10 publications

(5 citation statements)

References 3 publications

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“…This section introduces two Bayesian classifiers used for BCI: Bayes quadratic and hidden Markov model (HMM). Although Bayesian graphical network (BGN) has been employed for BCI, it is not described here as it is not common and, currently, not fast enough for real-time BCI [61,62]. All these classifiers produce nonlinear decision boundaries.…”

Section: Nonlinear Bayesian Classifiersmentioning

confidence: 99%

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A review of classification algorithms for EEG-based brain–computer interfaces

et al. 2007

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Section: Nonlinear Bayesian Classifiersmentioning

confidence: 99%

“…[20,23,57] (see table A1). Reference [79] is an exception, but the authors admitted that the chosen HMM architecture may not have been suitable. Dynamic classifiers probably are successful in BCI because they can catch the relevant temporal variations present in the extracted features.…”

Section: The Synchronous Bci the Synchronous Case Is The Most Widely ...mentioning

confidence: 99%

A review of classification algorithms for EEG-based brain–computer interfaces

et al. 2007

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“…In the other method, independent component analysis (ICA) [3,14] was used which resulted in a much better classification accuracy [8]. In this paper the results with the time filter are reported because the differences of classifiers were more distinct in this method.…”

Section: Purdue Dataset the Purdue Dataset Was Acquired Bymentioning

confidence: 99%

“…These classifiers are known methods in the machine learning literature. The Gaussian mixture model is represented as a Bayesian network, and this is the first time that such a classifier has been used for the EEG signal classification [8].…”

Section: Introductionmentioning

confidence: 99%

Different classification techniques considering brain computer interface applications

Rezaei¹,

Tavakolian²,

Nasrabadi³

et al. 2006

J. Neural Eng.

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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.

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“…Bayesian approaches have been applied successfully in brain-computer interface based on EEG signals as reported in Lemm et al ( 2004), Tavakolian and Rezaei (2004), Barreto et al (2004). The Bayes rule assigns the input feature pattern to the brain state which has the highest probability.…”

Section: Eeg Signal Classificationmentioning

confidence: 99%

Evidence-Based Combination of Weighted Classifiers Approach for Epileptic Seizure Detection using EEG Signals

Mohamed

Shaban

Mohamed

2012

International Journal of Knowledge Discovery in Bioinformatics

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Different brain states and conditions can be captured by electroencephalogram (EEG) signals. EEG-based epileptic seizure detection techniques often reduce these signals into sets of discriminant features. In this work, an evidence theory-based approach for epileptic detection, using several classifiers, is proposed. Within the framework of the evidence theory, each of these classifiers is considered a source of information and given a certain weight based on both its overall classification accuracy as well as its precision rate for the respective brain state. These sources are fused using the Dempster’s rule of combination. Experimental work is done where five time domain features are obtained from EEG signals and used by a set classifiers, namely, Bayesian, K-nearest neighbor, neural network, linear discriminant analysis, and support vector machine classifiers. Higher classification accuracy of 89.5% is achieved, compared to 75.07% and 87.71% accuracy obtained from the worst and best used classifiers.

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Classification of mental tasks using gaussian mixture bayesian network classifiers

Cited by 10 publications

References 3 publications

A review of classification algorithms for EEG-based brain–computer interfaces

A review of classification algorithms for EEG-based brain–computer interfaces

Different classification techniques considering brain computer interface applications

Evidence-Based Combination of Weighted Classifiers Approach for Epileptic Seizure Detection using EEG Signals

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