An improved feature extraction algorithms of EEG signals based on motor imagery brain-computer interface

Abstract

The electroencephalogram (EEG) signals based on the Brian-computer Interface (BCI) equipment is weak, non-linear, non-stationary and time-varying, so an effective feature extraction method is the key to improving the recognition accuracy. Electrooculogram and electrocardiogram artifacts are common noises in the process of EEG signals acquisition, it seriously affects the extraction of useful information. This paper proposes a processing method on EEG signals by combing independent component analysis (ICA), wavelet transform (WT) and common spatial pattern (CSP). First, the independent component analysis algorithm is used to break the EEG signals into independent components; and then these independent components are decomposed by WT to obtain the wavelet coefficient of each independent source. The soft and hard compromise threshold function is used to process the wavelet packet coefficients. Then the CSP algorithm is used to extract the features of the denoised EEG data. Finally, four common classification algorithms are used for classification to verify the effectiveness of the improved algorithm. The experimental results show that the EEG signals processed by the proposed method has obvious advantages in identify and remove electrooculogram (EOG) and electrocardiogram (ECG) artifacts, meanwhile, it can preserve the neural activity that is missed in the noise component. Cross-comparison experiments also proved that the proposed method has higher classification accuracy than other algorithms.