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

Dae-Hyeok Lee, Sung-Jin Kim, Si-Hyun Kim

TL;DR

This study demonstrates the feasibility of using deep learning techniques to classify different fatigue levels in pilots, specifically a normal state, low fatigue, and high fatigue, to be the first study to classify fatigue levels in 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.

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

TL;DR

This study demonstrates the feasibility of using deep learning techniques to classify different fatigue levels in pilots, specifically a normal state, low fatigue, and high fatigue, to be the first study to classify fatigue levels in 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.

Paper Structure

This paper contains 11 sections, 3 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. MATERIALS AND METHODS
  3. Subjects
  4. Experimental Environment
  5. Experimental Protocol and Paradigm
  6. Signal Preprocessing
  7. The Proposed Model
  8. RESULTS AND DISCUSSION
  9. Performance Evaluation
  10. Neurophysiological Analysis from EEG Signals
  11. CONCLUSION

Figures (3)

  • Figure 1: Data configuration of EEG and EOG channels.
  • Figure 2: Experimental paradigm for inducing fatigue in a simulated flight environment.
  • Figure 3: Scalp topographies according to the spectral bands ($\delta$--, $\theta$--, $\alpha$--, and $\beta$--bands) for a representative subject (S6). The locations of channels with the statistical significance are indicated as grey '$\ast$' (*: p$<$0.05).