Search

In motor-imagery-based brain–computer interfaces, the frequency, and spatial information of electroencephalography signals can be used to improve the performance of motor imagery classification. However, the problem of subject-specific frequency band selection occurs frequently in spatial feature extraction. In this study, to enhance the frequency information in a spatial filter, we design an upper triangle filter bank to determine discriminative frequency components and apply the common spatial pattern to extract spatial features from sub-bands. Furthermore, an autoencoder neural network is constructed to reduce the high dimensionality of spatial features. The classification performance of the proposed method is experimentally evaluated on motor imagery datasets. The proposed method provides more discriminative features and higher classification performance in comparison with competing algorithms. This proposed filter bank method can be used to extend the other spatial and spectral processing method for motor imagery classification.

This paper is focus on the analysis of the ability of iris segmentation method to process images with heterogeneous characteristics, simulating the dynamics of a non-cooperative environment. Aimed to improve the performance of iris segmentation method in non-cooperative environment, the authors introduce a new method. According to the different characteristics of the inner border and outer border of the iris, this new method segments iris by two steps: one step for inner border based on simple intensity operator, and the other step for outer border based on border recognition with support vector machine classifier. The performance of the proposed method and three selected traditional methods had been tested on the UBIRIS database. Compared with the traditional methods, the proposed method has more exactly accurate and less computation time on the image with poor quality. Also, the proposed method is higher robustness and less dependent on iris image conditions.