A Review of Convolutional Recurrent Neural Network Approaches

Abstract:-Fatigue is a critical factor affecting cognitive performance, decision-making, and overall well-being, particularly in high-risk domains such as transportation, healthcare, aviation, and industrial operations. Accurately predicting fatigue-related mental states is essential for preventing accidents and optimizing human efficiency. Recent advancements in deep learning, particularly Convolutional Recurrent Neural Networks (CRNNs), have shown significant promise in analyzing EEG signals for fatigue detection. CRNNs combine Convolutional Neural Networks (CNNs) for feature extraction and Recurrent Neural Networks (RNNs) for sequential pattern analysis, making them well-suited for identifying fatigue transitions over time. This review provides a comprehensive analysis of CRNN-based approaches for fatigue prediction, examining feature extraction techniques, preprocessing methods, model architectures, and performance evaluation metrics. We discuss various datasets used for fatigue detection, highlighting their advantages and limitations. Additionally, we compare CRNN models with traditional machine learning methods, such as Support Vector Machines (SVM), Random Forest (RF), and standard CNN-LSTM hybrids, demonstrating the superiority of CRNNs in capturing both spatial and temporal dependencies in EEG signals. The paper also explores the impact of attention mechanisms, feature selection strategies, and multimodal data integration in enhancing model accuracy. Finally, we address the challenges of real-time fatigue prediction, including cross-subject variability, data imbalance, and computational efficiency. Future research directions focus on integrating deep reinforcement learning, explainable AI (XAI), and edge computing for developing robust, real-time fatigue monitoring systems. By reviewing the latest advancements in CRNN-based fatigue prediction, this study aims to guide future research toward more accurate, interpretable, and scalable mental state monitoring solutions

---