Med Biol Eng Comput - Classification of multichannel EEG patterns using parallel hidden Markov models.

Tópicos

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Resumo

In this paper, a parallel hidden-Markov-model (PHMM)-based approach is proposed for the problem of multichannel electroencephalogram (EEG) patterns classification. The approach is based on multi-channel representation of the EEG signals using a parallel combination of HMMs, where each model represents a particular channel. The performance of the proposed algorithm is studied using an artificial EEG database, and two real EEG databases: a database of two classes of EEGs elicited during a task of imagery of hand upward and downward movements of a computer screen cursor (db Ia), and a database of two classes of sensorimotor EEGs elicited during a feedback-regulated left-right motor imagery task (db III). The results show that the proposed algorithm outperforms other commonly used methods with classification rate improvement of 2 and 10% for db Ia and db III, respectively. In addition, the proposed method outperforms a support vector machine classifier with a linear kernel, when both classifiers utilize the same feature set. The results also show that a model architecture which includes a left-to-right scheme with no skips, five states and three Gaussians, outperforms the other tested architectures due to the fact that it allows a better modeling of the temporal sequencing of the EEG components.

Resumo Limpo

paper parallel hiddenmarkovmodel phmmbase approach propos problem multichannel electroencephalogram eeg pattern classif approach base multichannel represent eeg signal use parallel combin hmms model repres particular channel perform propos algorithm studi use artifici eeg databas two real eeg databas databas two class eeg elicit task imageri hand upward downward movement comput screen cursor db ia databas two class sensorimotor eeg elicit feedbackregul leftright motor imageri task db iii result show propos algorithm outperform common use method classif rate improv db ia db iii respect addit propos method outperform support vector machin classifi linear kernel classifi util featur set result also show model architectur includ lefttoright scheme skip five state three gaussian outperform test architectur due fact allow better model tempor sequenc eeg compon

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