Key result
XGBoost and 1D-CNN+LSTM models emerge as top solutions for detecting pilots' mental states from physiological data.
Why the study?
Identifying pilots' mental states linked to attention-related human performance-limiting states using physiological data is challenged by data imbalance.
XGBoost and a combined 1D-CNN+LSTM model effectively detect pilots' mental states from physiological data, potentially improving aviation safety.
May support ML monitoring of pilot mental states; leaves open prospective validation before operational use.
View Video Presentation: https://doi.org/10.2514/6.2023-4529.vid This study focuses on identifying pilots' mental states linked to attention-related human performance-limiting states (AHPLS) using a publicly released, imbalanced physiological dataset. The research integrates electroencephalography (EEG) with non-brain signals, such as electrocardiogram (ECG), galvanic skin response (GSR), and respiration, to create a deep learning architecture that combines one-dimensional Convolutional Neural Network (1D-CNN) and Long Short-Term Memory (LSTM) models. Addressing the data imbalance challenge, the study employs resampling techniques, specifically downsampling with cosine similarity and oversampling using Synthetic Minority Over-sampling Technique (SMOTE), to produce balanced datasets for enhanced model performance. An extensive evaluation of various machine learning and deep learning models, including XGBoost, AdaBoost, Random Forest (RF), Feed-Forward Neural Network (FFNN), standalone 1D-CNN, and standalone LSTM, is conducted to determine their efficacy in detecting pilots' mental states. The results contribute to the development of efficient mental state detection systems, highlighting the XGBoost algorithm and the proposed 1D-CNN+LSTM model as the most promising solutions for improving safety and performance in aviation and other industries where monitoring mental states is essential.
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Alreshidi et al. (2023) studied Attention-related human performance-limiting states (AHPLS) in pilots. 1D-CNN+LSTM model and XGBoost algorithm vs. Other machine learning models (AdaBoost, Random Forest, FFNN, standalone 1D-CNN, standalone LSTM) was evaluated on Efficacy in detecting pilots' mental states. The XGBoost algorithm and a proposed 1D-CNN+LSTM model were identified as the most promising solutions for detecting pilots' mental states from imbalanced physiological data.
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