Intelligent EEG Artifact Removal in Motor ImageryBCI: Synergizing FCIF,FCFBCSP, and Modified DNN with SNR, PSD, and Spectral Coherence Evaluation

Motor Imagery Brain-Computer Interfaces (MI BCIs) face challenges due to artifacts in electroencephalogram (EEG) signals, hindering accurate decoding of imagined movements. This study introduces an innovative method for intelligent EEG artifact removal in MI BCIs. By synergizing Four Class Iterative Filtering (FCIF), Four Class FBCSP (FCFBCSP), and a Modified Deep Neural Network (DNN) with Signal-to-Noise Ratio (SNR), Power Spectral Density (PSD), and Spectral Coherence evaluation, our approach enhances feature extraction specificity and extends the Filter Bank Common Spatial Pattern (FBCSP) algorithm for four MI classes. The primary objective is to improve MI BCI reliability through refined artifact removal, thus advancing EEG signal processing for MI applications.