Merged CNNs for the classification of EEG motor imagery signals

A novel approach to classifying MI tasks using Deep Learning techniques that achieves accuracy, precision, recall, and F1 score values of 64.75%, 64.94%, 65.63%, and 64.13%, respectively, indicating its potential to enhance the accuracy of MI task classification within EEG signals.