Discussions on the Effect of Void Sampling to EEG Features Extracting in Two Types of Motor Imagery

• 1、School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001

Abstract：EEG samples usually contain void sampling data then make various effects on EEG identification. In the paper, the two-type motor imagery EEG data sets of the international BCI competitions was used as EEG samples, and the method of wavelet transform, moving average etc was used to extract various EEG features. From various aspects, including the proportion change of void sampling data, different methods of EEG feature extraction, and different sample size etc, the paper analyzed the effects of void sampling data on EEG identifying results. The study showed that void sampling data can cause feature missing, and then make different effects on different feature extraction methods, including decreased stability of EEG identification result, and the effect on the integrity of the identification model etc, however, the increase of sample size can increase the stability of the identification result. Therefore, to select the methods of EEG feature extraction, the factors of void sampling data content and sample size etc should be considered besides feature extraction methods.

Keywords： BEI (Biotic Electric Interface) EEG (electroencephalogram) wavelet transform sampling conformity rate