Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

Schizophrenia is a chronic, heterogeneous neurodevelopmental disorder that has complex symptoms and uncertain etiology. Mounting evidence indicates the involvement of genetics and epigenetic disturbances, alteration in gut microbiome, immune system abnormalities, and environmental influence in the disease, but a single root cause and mechanism involved has yet to be conclusively determined. Consequently, the identification of diagnostic markers and the development of psychotic drugs for the treatment of schizophrenia faces a high failure rate. This article surveys the etiology of schizophrenia with a particular focus on gut microbiota regulation and the microbial signaling system that correlates with the brain through the vagus nerve, enteric nervous system, immune system, and production of postbiotics. Gut microbially produced molecules may lay the groundwork for further investigations into the role of gut microbiota dysbiosis and the pathophysiology of schizophrenia. Current treatment of schizophrenia is limited to psychotherapy and antipsychotic drugs that have significant side effects. Therefore, alternative therapeutic options merit exploration. The use of psychobiotics alone or in combination with antipsychotics may promote the development of novel therapeutic strategies. In view of the individual gut microbiome structure and personalized response to antipsychotic drugs, a tailored and targeted manipulation of gut microbial diversity naturally by novel prebiotics (non-digestible fiber) may be a successful alternative therapeutic for the treatment of schizophrenia patients.

Workers with demanding jobs are at risk of experiencing mental stress, leading to decreased performance, mental illness, and disrupted sleep. To detect elevated stress levels in the workplace, studies have explored stress measurement from physiological, psychological, and behavioral perspectives. This paper reviews the assessment methods and strategies for mitigating mental stress in the workplace and provides recommendations for early detection and mitigation of mental stress. Among the modalities, Electroencephalography (EEG), Electrocardiography (ECG) and Galvanic Skin Response (GSR) were found to be the most used in assessing mental stress in the workplace. Nevertheless, these modalities are sensitive to motion artifacts and are difficult to be integrated into real work environments. To further improve stress level assessment in the workplace, multimodality integration with a reduced number of sensors such as EEG, GSR and Functional near infrared spectroscopy (fNIRS) can be utilized. This would lead to developing strategies for stress management in real-time. Furthermore, combining EEG with fNIRS would improve source localization of mental stress. To mitigate stress, we recommend developing a closed loop system that incorporates brain data acquisition systems and machine learning with physical stimulations such as audio Binaural Beats Stimulation and/or Transcranial Electric Stimulation.

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.

Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contain rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Over this, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

Worker productivity is a major concern for the construction industry. Many studies assessed the effect of various factors, such as the work environment and worker health, on productivity. Nevertheless, the extent to which an automatic productive assessment can benefit from wearable electronic-based sensor technologies for physiological and psychological tracking purposes has not yet been fully investigated. This work assesses the ability of capturing the effect of construction workers’ happiness on their productivity using physiological signals collected via wearable sensors. Data from both a traditional tracking process (human annotators) and an automated worker physiological signal tracking process that was designed for the purposes of this study were compiled. By considering the traditional tracking process as the baseline for the comparison, this study evaluated the effectiveness of automating happiness tracking as a leading indicator of construction workers’ productivity. The physiological signal data collected included blood volume pulse (BVP), respiration rate (RR), heart rate (HR), galvanic skin response (GSR), and skin temperature (TEMP). These data were obtained from a 4-day field study conducted at a pre-fabricated stone construction factory. The study concluded that a moderate positive correlation exists between a worker’s emotional status and his productivity exists, with a p-value = 5.5×10-8 and a Pearson’s coefficient of 0.43.