Decoding EEG-based Workload Levels Using Spatio-temporal Features Under
  Flight Environment
Dae-Hyeok Lee Sung-Jin Kim Si-Hyun Kim Seong-Whan Lee
The detection of pilots' mental states is important due to the potential for
their abnormal mental states to result in catastrophic accidents. This study
introduces the feasibility of employing deep learning techniques to classify
different workload levels, specifically normal state, low workload, and high
workload. To the best of our knowledge, this study is the first attempt to
classify workload levels of pilots. Our approach involves the hybrid deep
neural network that consists of 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 within the simulated flight environment. In contrast to
four conventional models, our proposed model achieved a superior grand--average
accuracy of 0.8613, surpassing other conventional models by at least 0.0597 in
classifying workload levels across all participants. Our model not only
successfully classified workload levels but also provided valuable feedback to
the participants. Hence, we anticipate that our study will make the significant
contributions to the advancement of autonomous flight and driving leveraging
artificial intelligence technology in the future.