This paper explores a methodology for integrating deep learning techniques into personalized human-robot collaboration (HRC) systems by decoding individual EEG-based neurophysiological patterns. Rather than focusing on biometric identity recognition, the proposed approach centers on detecting individual typological features of EEG dynamics – unique cognitive-motor response patterns that emerge during interaction with robotic systems. EEG data were collected from 30 participants under standardized conditions, with frontal channels (F3, F4, F7, F8) used for signal acquisition. The study introduces a targeted adaptation of classical CNN architectures – AlexNet and MobileNetV2 – to the structural and dynamic characteristics of EEG spectrograms, including low spatial resolution, single-channel input, and domain-specific noise. Two adapted models – SimpleAlexNet and LiteMobileNet2D – were evaluated for their ability to classify EEG-based pattern types under constrained computational conditions. Experimental results show that LiteMobileNet2D achieves a superior balance between accuracy and generalization, maintaining low overfitting despite aggressive model simplification. SimpleAlexNet, while prone to overfitting, demonstrated acceptable performance in medium-complexity tasks. These findings confirm that individualized EEG pattern recognition is feasible with lightweight neural models, enabling real-time adaptation of HRC behavior to the operator’s current physiological state and laying a foundation for scalable, context-aware HRC systems.