Key result
Gamma deep belief network combined with Wigner-Ville distribution accurately detects pilot brain fatigue.
Why the study?
Pilots' brain fatigue status recognition faces challenges in extracting brain cognitive features and identifying fatigue characteristics.
A novel gamma deep belief network combined with a smoothed pseudo affine Wigner-Ville distribution method effectively extracts and identifies brain fatigue characteristics from EEG signals in pilots.
May support EEG fatigue monitoring tools for pilots; leaves open prospective validation before clinical use.
Pilots' brain fatigue status recognition faces two important issues. They are how to extract brain cognitive features and how to identify these fatigue characteristics. In this article, a gamma deep belief network is proposed to extract multilayer deep representations of high-dimensional cognitive data. The Dirichlet distributed connection weight vector is upsampled layer by layer in each iteration, and then the hidden units of the gamma distribution are downsampled. An effective upper and lower Gibbs sampler is formed to realize the automatic reasoning of the network structure. In order to extract the 3-D instantaneous time-frequency distribution spectrum of electroencephalogram (EEG) signals and avoid signal modal aliasing, this article also proposes a smoothed pseudo affine Wigner-Ville distribution method. Finally, experimental results show that our model achieves satisfactory results in terms of both recognition accuracy and stability.
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Wu et al. (2020) studied Pilots' brain fatigue status. Gamma deep belief network and smoothed pseudo affine Wigner-Ville distribution method was evaluated on Recognition accuracy and stability. A proposed gamma deep belief network combined with a smoothed pseudo affine Wigner-Ville distribution method achieved satisfactory recognition accuracy and stability for detecting pilots' brain fatigue.