Performance Evaluation of Compressed Deep CNN for Motor Imagery Classification using EEG

Recently, deep learning and convolutional neural networks (CNNs) have reported several promising results in the classification of Motor Imagery (MI) using Electroencephalography (EEG). With the gaining popularity of CNN-based BCI, the challenges in deploying it in a real-world mobile and embedded device with limited computational and memory resources need to be explored. Towards this objective, we investigate the impact of the magnitude-based weight pruning technique to reduce the number of parameters of the pre-trained CNN-based classifier while maintaining its performance. We evaluated the proposed method on an open-source Korea University dataset which consists of 54 healthy subjects' EEG, recorded while performing right-and left-hand MI. Experimental results demonstrate that the subject-independent model can be maximumly pruned to 90% sparsity, with a compression ratio of 4.77× while retaining classification accuracy at 84.44% with minimal loss of 0.02% when compared to the baseline model's performance. Therefore, the proposed method can be used to design more compact deep CNN- based BCIs without compromising on their performance.