LAUSR

In order to fully extract the temporal and spatial features contained in motor imagery electroencephalography (EEG) signals for effective identification of motor imagery, a three-dimensional capsule network (3D-CapsNet) EEG signal recognition model is proposed, which can integrate the MI-EEG temporal dimension, channel spatial dimension and the intrinsic relationship between features to maximize the feature representation capability. Firstly, a multi-layer 3D convolution module is used to extract features in the time and inter-channel space dimensions as the low-level features. Secondly, advanced spatial features are also obtained through capsule network integration. Finally, dynamic routing connections and squash functions are applied for classification. The experimental analysis is conducted on the BCI competition IV dataset 2a. The proposed model performs well on all the subjects’ datasets, such that the average accuracy and average Kappa value of 9 subjects are 84.028% and 0.789, respectively. The experimental results confirm effectiveness of the proposed method. Additionally, accuracy of the four-class classification is improved, and the impact of individual variability is overcome to a certain extent.