Feature Attraction and Classification of Mental EEG Using Approximate Entropy

Author

Zhou, Weidong ; Zhong, Linghui ; Zhao, Hao

Author_Institution

Coll. of Inf. Sci. & Eng., Shandong Univ., Jinan

fYear

2006

Firstpage

5975

Lastpage

5978

Abstract

The approximate entropy (ApEn), which is a new statistical method to measure the complexity of sequences, was introduced in this paper. First, the EOG artifact was removed from the EEG using the method of independent component analysis (ICA). Then ApEn was used to analyze the mental EEG signals to extract the features for pattern identification and task classification. The simulations showed that the classification accuracy is high and the proposed methods are effective

Keywords

electro-oculography; electroencephalography; entropy; feature extraction; independent component analysis; medical signal processing; signal classification; EOG artifact removal; approximate entropy; feature attraction; feature extraction; independent component analysis; mental EEG; sequence complexity; statistical method; task classification; Brain modeling; Electroencephalography; Electrooculography; Entropy; Feature extraction; Independent component analysis; Pattern analysis; Signal analysis; Signal processing; Statistical analysis; Approximate Entropy(ApEn); Complexity; Independent Component Analysis(ICA); Mental EEG;

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.2005.1615852

Filename

1615852