EEG-Based Pilot Emotion Monitoring using KNN in Maneuvering Conditions

Abstract

Flight safety is of paramount importance, and preventive measures are essential to maintain stable airline performance while minimizing accidents linked to pilots' emotional responses. This study investigates pilots' fear reactions during flight simulations using electroencephalography (EEG) signals and the K-Nearest Neighbors (KNN) algorithm, chosen for its simplicity and high classification accuracy of 94.08%. By analyzing brainwave patterns, this study highlights the connection between pilots' mental states, including attention levels at varying altitudes, and overall flight performance. The findings contribute to the aviation industry's efforts to enhance safety by understanding and mitigating emotional factors that could affect pilot decision-making.