This paper compares different deep learning models for analyzing EEG brain signals to recognize cognitive states like attention, fatigue, and memory tasks (N-Back). It evaluates models such as Hybrid NeuroNet, DCN-ResNet, and EEGFormer using different training methods to see which gives the best accuracy and performance.