EEG based Stress Analysis through Feature Extraction

The diagnosis of Stress relies virtually solely on doctor-patient conversation and scale analysis, which includes problems such as patient denial, insensitivity, subjective biases, and inaccuracy. Improving the accuracy of Stress diagnosis and therapy necessitates the development of an objective, computerized system for predicting clinical outcomes. Using the modification of EEG data and machine learning techniques, this study attempts to improve the recognition of Stress. The EEG data of 10 volunteers were acquired using a Narosky device during an experiment, including emotive facial stimuli. Psychiatrists used the EEG signal as the criterion for diagnosis of Stress in patients. The different approaches processed the features: machine learning and deep learning. Significant outcomes are achieved using PCA, ICA & EMD for BCI applications. SVM empowers a developer with several advantages: PCA exhibits excellent generalization properties, with stress & pressure detection using EEG Signals. If the signals are negative, the impact of overtraining is sensitive to the curse-of- dimensionality. These advantages were achieved by using EEG signals to detect Stress. The experimental analysis gives some overview of all different approaches, which depend on frequency domain analysis with 14 fourteen-channel EEG signals with reasonable accuracy

Evaluation of Data Processing and Artifact Removal Approaches Used for Physiological Signals Captured Using Wearable Sensing Devices during Construction Tasks

Wearable sensing devices (WSDs) have enormous promise for monitoring construction worker safety. They can track workers and send safety-related information in real time, allowing for more effective and preventative decision making. WSDs are particularly useful on construction sites since they can track workers’ health, safety, and activity levels, among other metrics that could help optimize their daily tasks. WSDs may also assist workers in recognizing health-related safety risks (such as physical fatigue) and taking appropriate action to mitigate them. The data produced by these WSDs, however, is highly noisy and contaminated with artifacts that could have been introduced by the surroundings, the experimental apparatus, or the subject’s physiological state. These artifacts are very strong and frequently found during field experiments. So, when there is a lot of artifacts, the signal quality drops. Recently, artifacts removal has been greatly enhanced by developments in signal processing, which has vastly enhanced the performance. Thus, the proposed review aimed to provide an in-depth analysis of the approaches currently used to analyze data and remove artifacts from physiological signals obtained via WSDs during construction-related tasks. First, this study provides an overview of the physiological signals that are likely to be recorded from construction workers to monitor their health and safety. Second, this review identifies the most prevalent artifacts that have the most detrimental effect on the utility of the signals. Third, a comprehensive review of existing artifact-removal approaches were presented. Fourth, each identified artifact detection and removal approach was analyzed for its strengths and weaknesses. Finally, in conclusion, this review provides a few suggestions for future research for improving the quality of captured physiological signals for monitoring the health and safety of construction workers using artifact removal approaches