Towards adaptive flight support: EEG-based cognitive workload monitoring in aviation scenarios
Year: 2026
Type: article
Abstract: OBJECTIVES: Cognitive load is a critical factor that influences aviation safety. During complex flight operations, pilots' cognitive load increases significantly, which may adversely affect flight performance. METHODS: To explore this relationship, a simulated flight using a Cessna 172 platform was conducted, during which Electroencephalography (EEG) signals were recorded across different flight phases. In parallel, pilots' subjective workload was assessed using the NASA-TLX scale. Frequency-domain and functional connectivity analyses were subsequently employed to assess the pilots' cognitive load based on EEG features and brain network connectivity patterns. RESULTS: The results revealed that during take-off, turning, and landing phases, significant alterations in EEG activity were observed in the frontal regions, with alpha and beta rhythms exhibiting phase-dependent desynchronization. Furthermore, both the global efficiency and transitivity of brain functional networks in the alpha and beta bands exceeded those observed during the cruising phase. The Phase Locking Value (PLV) heatmaps and partial directed coherence (PDC) analyses also demonstrated increased frequency and complexity of inter-regional information transfer during these high-demand phases. CONCLUSIONS: These findings suggest that EEG-based cognitive load assessment offers a valuable foundation for developing pilot support systems and adaptive flight control strategies, thereby enhancing flight performance and aviation safety.
Authors: Li Ji, Quancao Sun, Jingming Wan, Xinxi Zhang, Leiye Yi
Institution: Shenyang Aerospace University
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid:
OpenAlex ID: W7212031506