In recent days, mental health plays an important role in leading a peaceful life. When we consider mental health, stress, anxiety, fear comes into picture. Most people experience stress from time to time. The stress will be triggered by any event which puts an individual in a state of nervousness or frustration. The reasons for the cause of this stress are many. There is a need to analyze the level of stress in an individual beforehand and help him with proper direction to overcome the stress. This paper throws light on the analysis of the stress using the Brain Computer Interface, Open BCI. This will provide an easy monitoring of stress at the comfort of our home. The collection and analysis of the data through Open BCI comes up with many challenges like the range of the data collected, the noise in the data, raw data, unanswered spikes in the plots. Tackling these things will ease the use of the Open BCI device to a greater level. The use of band pass filters, notch filters and brainflow packages support in tackling the problems. The paper encapsulates the challenges that are prone to occur during the initial phase of data collection and analysis and presents a method for cleaning the raw data obtained.

Analysis Of EEG Signals Using Open BCI To Predict The Stress Level

In recent days, mental health plays an important role in leading a peaceful life. When we consider mental health, stress, anxiety, fear comes into picture. Most people experience stress from time to time. The stress will be triggered by any event which puts an individual in a state of nervousness or frustration. The reasons for the cause of this stress are many. There is a need to analyze the level of stress in an individual beforehand and help him with proper direction to overcome the stress. This paper throws light on the analysis of the stress using the Brain Computer Interface, Open BCI. This will provide an easy monitoring of stress at the comfort of our home. The collection and analysis of the data through Open BCI comes up with many challenges like the range of the data collected, the noise in the data, raw data, unanswered spikes in the plots. Tackling these things will ease the use of the Open BCI device to a greater level. The use of band pass filters, notch filters and brainflow packages support in tackling the problems. The paper encapsulates the challenges that are prone to occur during the initial phase of data collection and analysis and presents a method for cleaning the raw data obtained. 

In this competitive era, students are undergoing a lot of stress due to societal influence, financial status, and academic environment. This is leading to many psychological disorders such as depression, anxiety, and many others. One student commit suicide every hour in India, and in most of the cases, stress and pressure are the main reasons for the deadly attempt. The high level of stress is hindering the performance of the students in the academics. It is necessary to find out the stress level of the students and guide them through different techniques that help them in reducing stress levels. There are many ways to investigate the level of stress among the students. Techniques like psychological questionnaires, physiological measures like blood pressure, salivary alpha amylase, and vagal tone can be considered for analyzing stress in students. Perceived stress scale (PSS) is one of the standard psychological questionnaire methods used to analyze stress level of an individual. It is a measure of degree to which situations in one’s life are appraised as stress. The scale includes a series of general queries about present level of stress experience. The scale is free of content specific to any subpopulation group. The PSS is used to categorize the subjects as less stressed, moderately stressed, and highly stressed. The data through responses from 486 engineering students was collected to investigate the stress level of the students. The data thus obtained is validated using the statistical tool ANOVA. ANOVA test is a way to find out if experimental results are significant. The gathered data is validated for statistically significant group and used for further analysis. The results from the analysis of PSS showed that the female students are less stressed when compared to the male students. The result can further be imbibed in an intelligent system which could detect the level of stress and suggest a method or technique to the subject so as to reduce the level of stress.

Shrivatsa D. Perur

Papers

Statistical Analysis of Stress Levels in Students Pursuing Professional Courses