An in-flight multimodal data collection method for assessing pilot cognitive states and performance in general aviation

Human factors are central to aviation safety, with pilot cognitive states such as workload, stress, and situation awareness playing important roles in flight performance and safety. Although flight simulators are widely used for training and scientific research, they often lack the ecological validity needed to replicate pilot cognitive states from real flights. To address these limitations, a new in-flight data collection methodology for general aviation using a Cessna 172 aircraft, which is one of the most widely used aircraft for pilot training, is presented. The dataset combines: • Human data from wearable physiological sensors (electroencephalography, electrocardiography, electrodermal activity, and body temperature) and eye-tracking glasses. • Flight data from ADS-B flight recorder. • Pilot's self-reported cognitive states and flight performance rate by instructor. The paper describes the sensor setup, flight task design, and data synchronization procedures. Potential analyses using statistical and machine learning methods are discussed to classify cognitive states and demonstrate the dataset's value. This methodology supports human factors research and has practical value for applications in pilot training, performance evaluation, and aviation safety management. The method was applied in a field study with 25 participants, from which 20 complete multimodal datasets were retained after data cleaning. After collecting additional data, the resulting dataset will support further research on pilot performance and behavior.