Character Encoding-Based Motor Imagery EEG Classification Using CNN

Abstract

This letter proposes a simple and effective EEG classification method using convolutional neural networks (CNNs). The EEG signals are first converted to character sequences in order to simplify feature extraction and meanwhile reduce data dimensionality. The character sequences are then transformed according to binary encoding and finally fed into a specific CNN, which gives consideration to both classification accuracy and computation efficiency. Experimental simulations show that the proposed method has the advantages of simple preprocessing and lightweight network structure and that the average accuracy on two BCI Competition datasets can reach 94.36% and 81.8%, whereas the average accuracy on the self-made dataset is 71.67%, outperforming traditional methods. Furthermore, the proposed algorithm has been successfully applied to control the robotic arm of an EEG-based manipulator system, further validating its feasibility.