A Novel Two-Stage Refine Filtering Method for EEG-Based Motor Imagery Classification.

ABSTRACT: 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 was proposed, inspired by Gradient-weighted Class Activation Mapping (Grad-CAM), which uses the gradients of any target concept flowing into the final convolutional layer to highlight the important part of training data for predicting the concept. In the first stage, MI classification was carried out and then the frequency band to be filtered was calculated

SUBMITTER: Yan Y 

PROVIDER: S-EPMC8440963 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature