A pilot control intention recognition method based on EEG in simulated flights

Pilot control intentions reflect the subjective desire to manipulate aircraft attitude through specific maneuvers. Accurate recognition of pilot control intentions is crucial for the development of autopilot systems and active safety technologies in flight control. A significant challenge arises from the similarity in workload between takeoff and landing, which complicates the identification of climb and descent intentions. This paper proposes an approach using a spatial attention EEGNet (SA-EEGNet) to identify pilot control intentions based on electroencephalography (EEG) signals. To address issues related to convolutional kernel sharing and network complexity, receptive field attention and spatial convolution were incorporated to enhance feature extraction and reduce redundancy. Designed for three-class classification, SA-EEGNet achieves 95% accuracy in subject-dependent data (5-fold cross-validation) and 93% accuracy in subject-independent data (7-fold cross-validation).