A multi-classification algorithm based on multi-domain information fusion for motor imagery BCI

Abstract

The current problem of motor imagery Electroencephalogram(EEG) signal classification is low classification accuracy and fixed EEG channel selection. We proposed a novel classification algorithm for motor imagery EEG signals, which overcomes the contradiction between the number of channels and the representational ability of features. Higher classification accuracy is achieved using less number of channels. The algorithm makes a combination of time windows, filter banks, and an optimal sorting of the projection space to reveal multi-domain information. Experiments based on the two datasets of BCI Competition have proved that the channel selection strategy used in this paper can adapt to the subject’s neural information and select the optimal channel combination. The feature extraction algorithm proposed can achieve excellent classification accuracy (77.7 %) and kappa value (0.70). The results are improved by 26.2 % compared to the One Versus One-Common Spatial Pattern (OVO-CSP) method and by 8.2 % compared to the One Versus One-Filter bank common spatial pattern (OVO-FBCSP) method. Additionally, the proposed method has outperformed to the other state-of-the-art methods using the same data set in terms of the performance. The proposed methodology can be employed as a promising tool for a motor imagery BCI device.