A multi-view CNN encoding for motor imagery EEG signals

Objective: The convolution neural network (CNN) has gained lots of attentions recently in decoding electroen-cephalogram (EEG) signals in Motor Imagery (MI) Brain-Computer Interface (BCI) designed for improving stroke rehabilitation strategies. However, the extremely non-linear, nonstationary nature of the EEG signals and diversity among individual subjects results in the overfitting of a CNN model and limits its learning ability. In this study, a densely connected convolutional network with multi-view inputs is proposed.Methods: First, different data subsets from the original EEG signals are created as the CNN model inputs through bandpass filters applied to the EEG signals to generate multiple frequency sub-band signals based on brain rhythms. Then, temporal and spatial features are captured based on the whole frequency band and the filtered sub-band signals, respectively. Further, two dense blocks with multi-CNN layers, which connect each layer to every other layer in the feed-forward path, are used to enhance the model learning capabilities and strengthen information propagation. Finally, a concatenation fusion method is used to integrate the extracted features and a fully connected layer for finalizing the classification. Results: The proposed method achieves an average accuracy of 75.16% on the public Korea University EEG dataset which consists the EEG signals of 54 healthy subjects for the two-class motor imagery tasks, higher than other state-of-the-art deep learning methods.Conclusion: The proposed method effectively extracts much richer motor imagery information from the EEG signals in the BCI system and improves the classification accuracy.

Automated summary

In this study, a densely connected convolutional network with multi-view inputs is proposed to decode EEG signals in the Motor Imagery BCI for stroke rehabilitation strategies. The method captures temporal and spatial features based on the whole frequency band and the filtered sub-band signals, and uses dense blocks to enhance model learning capabilities. The proposed method achieves higher accuracy than other deep learning methods.