Development of P300 detection by Combination of Time, Frequency and Spatial Feature Extraction Methods

Amini, Zahra; Abootalebi, Vahid; Sadeghi, Mohammad Taghi

doi:10.22041/ijbme.2011.13198

Document Type : Full Research Paper

Authors

¹ M.Sc Student, Electrical and Computer Engineering Department, Yazd University

² 2Assistant Professor, Electrical and Computer Engineering Department, Yazd University

³ Assistant Professor, Electrical and Computer Engineering Department, Yazd University

10.22041/ijbme.2011.13198

Abstract

The aim of this paper is to design a pattern recognition based system to detect P300 component in multi-channel electroencephalogram (EEG) trials. This system has two main blocks, feature extraction and classification. In feature extraction block, in addition to conventional features namely morphological, frequency and wavelet features, some new features included intelligent segmentation, common spatial pattern (CSP) and combined features (CSP + Segmentation) have also been used. Three criteria were used for evaluation and selection of a feature set by choosing a subset of the original features that contains most of essential information. Firstly, a statistical analysis has been applied for evaluating the fitness of each feature in discriminating between target and non target signals. Secondly, each of these six groups of features was evaluated by a Linear Discriminant Analysis (LDA) classifier. Furthermore by using Stepwise Linear Discriminant Analysis (SWLDA), the best set of features was selected. Among these six feature vectors, intelligent segmentation was seen to be most efficient in classification of these signals. In classification phase, two linear classifiers -LDA and SWLDA- were used. The algorithm was described here has tested with dataset II from the BCI competition 2005. In this research, the best result for P300 detection is 97.05% .This result have proven to be more accurate than the results of previous works carried out in this filed.

Keywords

References

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Iranian Journal of Biomedical Engineering

Development of P300 detection by Combination of Time, Frequency and Spatial Feature Extraction Methods

References

References

Volume 4, Issue 4 - Serial Number 4
June 2010
Pages 293-306

Development of P300 detection by Combination of Time, Frequency and Spatial Feature Extraction Methods

References

References

Volume 4, Issue 4 - Serial Number 4June 2010Pages 293-306

Volume 4, Issue 4 - Serial Number 4
June 2010
Pages 293-306