Decoding Fatigue Levels of Pilots Using EEG Signals with Hybrid Deep Neural Networks

TL;DR

This study introduces a hybrid deep neural network model that accurately classifies pilots' fatigue levels using EEG signals, demonstrating potential for enhancing safety in aviation and autonomous systems.

Contribution

First to classify pilot fatigue levels with a hybrid deep neural network using EEG signals, achieving high accuracy and providing real-time feedback.

Findings

Achieved 88.01% accuracy in fatigue classification

Outperformed four conventional models by at least 5.99%

Validated in simulated flight environment with ten pilots

Abstract

The detection of pilots' mental states is critical, as abnormal mental states have the potential to cause catastrophic accidents. This study demonstrates the feasibility of using deep learning techniques to classify different fatigue levels, specifically a normal state, low fatigue, and high fatigue. To the best of our knowledge, this is the first study to classify fatigue levels in pilots. Our approach employs the hybrid deep neural network comprising five convolutional blocks and one long short-term memory block to extract the significant features from electroencephalography signals. Ten pilots participated in the experiment, which was conducted in a simulated flight environment. Compared to four conventional models, our proposed model achieved a superior grand-average accuracy of 0.8801, outperforming other models by at least 0.0599 in classifying fatigue levels. In addition to successfully classifying fatigue levels, our model provided valuable feedback to subjects. Therefore, we anticipate that our study will make the significant contributions to the advancement of autonomous flight and driving technologies, leveraging artificial intelligence in the future.