Cross-Subject Cognitive State Assessment for Unmanned System Operators Based on Brain Functional Connectivity

During the operation of Unmanned Aerial Vehicles (UAVs), the cognitive state of operators is prone to decline, posing a risk to task performance. However, many existing cognitive state assessment methods rely directly on raw electroencephalography (EEG) signals, yet exhibit limited robustness when applied across different individuals. To address this limitation and leverage the spatial information and inter-electrode relationships effectively captured by brain functional connectivity networks, this paper proposes an assessment method based on functional connectivity networks. Data from ten participants under three cognitive states were used to train and test various models on a per-subject basis, where each participant’s data was partitioned into separate training and testing sets. The results demonstrate that the proposed method achieves a mean recognition accuracy of 98.76% with a variance of 0.0113, representing an improvement of at least 7.01% in accuracy and a reduction of at least 0.0191 in variance compared to conventional approaches. This approach facilitates timely cognitive state identification, thereby enhancing the reliability of human–machine interaction in unmanned systems.