A Novel Two-Stage Refine Filtering Method for EEG-Based Motor Imagery Classification - Yan, Yuxin; Zhou, Haifeng; Huang, Lixin; Cheng, Xiao; Kuang, Shaolong (Frontiers in Neuroscience, 2021)
Cerebral stroke is a common disease across the world and it is a promising method to recognize the intention of stroke patients with the help of Brain computer Interface (BCI). In the field of motor imagery (MI) classification, appropriate filtering is vital for feature extracting of electroencephalogram (EEG) signals and consequently influences the accuracy of MI classification. In this case, a novel two stage refine filtering method inspired by Grad-CAM was proposed to solve the filtering problems. In the first stage, MI classification was carried out and then the frequency band to be filtered was calculated according to the Grad-CAM of the MI classification results. In the second stage, EEG was filtered and classified for a higher classification accuracy. To evaluate the filtering effect, this method was applied to the multi-branch neural network proposed in our previous work. Experiment results revealed that the proposed method reached state-of-the-art classification kappa value level and acquired at least 3 percent higher kappa value than other methods This study also proposed some promising application scenarios with this filtering method.