Title :
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
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;
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
DOI :
10.1109/IEMBS.2005.1615852
