An advanced bispectrum features for EEG-based motor imagery classification

Abstract

Motor imagery (MI)-related brain activities can be effectively described by frequency analysis. Bispectrum is developed to overcome the drawback of power spectrum that the estimation of power spectrum discards the phase relationship among frequency components. However, the widely used bispectral features extraction method adds up all bispectral values as one feature, which could lead to the loss of effective information and increase of the sensitivity to non-linear and non-Gaussian noises. Thus, the representative bispectral features extraction method may be inefficient for MI classification. In addition, recent research suggests that the variations of EEG signals could provide more useful underlying information of event-related brain responses. This paper presents an advanced variations based bispectral feature extraction method to improve the performance of MI classification. The proposed method calculates the variations of MI-related EEG signals as input to bispectrum estimation. Besides, a new segmented bispectral sum features are developed to reduce the influence of non-linear and non-Gaussian noises and emphasize the valuable information for MI classification. The dataset collected in our laboratory and BCI Competition IV dataset 2b were adopted to validate the proposed method. The results indicate that the proposed method outperforms the power spectrum based methods and the representative bispectral features based methods. Moreover, compared to other state-of-the-art works, our approach also achieves the greater performance for MI classification.