Researchers led by Wojcik in a Nature Scientific Reports article examined various deep learning models to discriminate between EEG signals during guided imagery relaxation and mental workload tasks. Their analysis compared 1D-CNN, LSTM, and hybrid architectures, demonstrating that focused data processing using cognitive electrode subsets can enhance classification accuracy significantly. This work offers promising directions for advances in brain-computer interface design.