Assessment of Pilot’s Mental Load During Traffic Pattern with Simulator EEG Data

Abstract

Conventional airfield traffic pattern simulation flight experiments are conducted for flight cadets. Wavelet packet decomposition and Hilbert-Huang transform algorithm are used to process EEG data in simulated flight, while energy distribution and spectrum characteristics of EEG in different flight phases are analyzed. The results show that there is a positive correlation between brain load and energy in the high frequency band above 10Hz, the energy level of landing and turning will be relatively improved, and the EEG energy at the moment of landing will reach peak in a short-term, which is easily recognized in the band. The statistical results show that the relative energy levels of $\boldsymbol{\alpha} / \boldsymbol{\beta}$ and $\theta / \alpha$ can be used to assess the mental load, which is also supported by SVM analysis results.