Decoding motor execution and motor imagery from EEG with deep learning and source localization

Abstract

The use of noninvasive imaging techniques has become pivotal in understanding human brain functionality. While modalities like MEG and fMRI offer excellent spatial resolution, their limited temporal resolution, often measured in seconds, restricts their application in real-time brain activity monitoring. In contrast, EEG provides superior temporal resolution, making it ideal for real-time applications in brain–computer interface systems. In this study, we combined deep learning with source localization to classify two motor task types: motor execution and motor imagery. For motor imagery tasks—left hand, right hand, both feet, and tongue—we transformed EEG signals into cortical activity maps using Minimum Norm Estimation (MNE), dipole fitting, and beamforming. These were analyzed with a custom ResNet CNN, where beamforming achieved the highest accuracy of 99.15%, outperforming most traditional methods. For motor execution involving six types of reach-and-grasp tasks, beamforming achieved 90.83% accuracy compared to 56.39% from a sensor domain approach (ICA + PSD + TSCR-Net). These results underscore the significant advantages of integrating source localization with deep learning for EEG-based motor task classification, demonstrating that source localization techniques greatly enhance classification accuracy compared to sensor domain approaches.