AGL-Net: An Efficient Neural Network for EEG-Based Driver Fatigue Detection

Abstract

Background: In recent years, road traffic safety has become a prominent issue due to the worldwide proliferation of vehicles on roads. The challenge of driver fatigue detection involves balancing the efficiency and accuracy of the detection process. While various detection methods are available, electroencephalography (EEG) is considered the gold standard due to its high precision in terms of detecting fatigue. However, deep learning models for EEG-based fatigue detection are limited by their large numbers of parameters and low computational efficiency levels, making it difficult to implement them on mobile devices. Methods: To overcome this challenge, an attention-based Ghost-LSTM neural network (AGL-Net) is proposed for EEG-based fatigue detection in this paper. AGL-Net utilizes an attention mechanism to focus on relevant features and incorporates Ghost bottlenecks to efficiently extract spatial EEG fatigue information. Temporal EEG fatigue features are extracted using a long short-term memory (LSTM) network. We establish two types of models: regression and classification models. In the regression model, we use linear regression to obtain regression values. In the classification model, we classify features based on the predicted values obtained from regression. Results: AGL-Net exhibits improved computational efficiency and a more lightweight design than existing deep learning models, as evidenced by its floating-point operations per second (FLOPs) and Params values of 2.67 M and 103,530, respectively. Furthermore, AGL-Net achieves an average accuracy of approximately 87.3% and an average root mean square error (RMSE) of approximately 0.0864 with the Shanghai Jiao Tong University (SJTU) Emotion EEG Dataset (SEED)-VIG fatigued driving dataset, indicating its advanced performance capabilities. Conclusions: The experiments conducted with the SEED-VIG dataset demonstrate the feasibility and advanced performance of the proposed fatigue detection method. The effectiveness of each AGL-Net module is verified through thorough ablation experiments. Additionally, the implementation of the Ghost bottleneck module greatly enhances the computational efficiency of the model. Overall, the proposed method has higher accuracy and computational efficiency than prior fatigue detection methods, demonstrating its considerable practical application value.

Keywords

• driver fatigue detection
• electroencephalogram (eeg)
• deep learning
• lightweight architecture