Feature Extraction of Motor Imagery in BCI with Approximate Entropy

Approximate Entropy (ApEn) is a regularity statistic that quantifies the unpredictability of fluctuations in a time series and can classify complex systems. This study, ApEn is used to extract features from motor imagery Electroencephalogram (EEG) that containing two motor imagery tasks. This method is different from the traditional way to get the character from EEG. Appropriate parameters must been chosen for different dataset. Two datasets are used in this paper. One comes from BCI Competion2003, the other from the lab of Electromagnetic theory and artifical intelligence, Chongqing University. The results show that ApEn can extract different features in left-hand and right-hand motor imagery EEG efficiently. Comparison between ApEn and Fast Fourier Transform (FFT) shows that ApEn has good capability to the two imagery tasks Brain Computer Interface.

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