Mental stress is a common problem that affects individuals all over the world. Stress reduces human functionality during routine work and may lead to severe health defects. Early detection of stress is important for preventing diseases and other negative health-related consequences of stress. Several neuroimaging techniques have been utilized to assess mental stress, however, due to its ease of use, robustness, and non-invasiveness, electroencephalography (EEG) is commonly used. This paper aims to fill a knowledge gap by reviewing the different EEG-related deep learning algorithms with a focus on Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the evaluation of mental stress. The review focuses on data representation, individual deep neural network model architectures, hybrid models, and results amongst others. The contributions of the paper address important issues such as data representation and model architectures. Out of all reviewed papers, 67% used CNN, 9% LSTM, and 24% hybrid models. Based on the reviewed literature, we found that dataset size and different representations contributed to the performance of the proposed networks. Raw EEG data produced classification accuracy around 62% while using spectral and topographical representation produced up to 88%. Nevertheless, the roles of generalizability across different deep learning models and individual differences remain key areas of inquiry. The review encourages the exploration of innovative avenues, such as EEG data image representations concurrently with graph convolutional neural networks (GCN), to mitigate the impact of inter-subject variability. This novel approach not only allows us to harmonize structural nuances within the data but also facilitates the integration of temporal dynamics, thereby enabling a more comprehensive assessment of mental stress levels.

Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine.